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ASEERVATHAM, SUJEEVAN. "A CONCEPT VECTOR SPACE MODEL FOR SEMANTIC KERNELS." International Journal on Artificial Intelligence Tools 18, no. 02 (April 2009): 239–72. http://dx.doi.org/10.1142/s0218213009000123.
Повний текст джерелаАнотація:
Kernels are widely used in Natural Language Processing as similarity measures within inner-product based learning methods like the Support Vector Machine. The Vector Space Model (VSM) is extensively used for the spatial representation of the documents. However, it is purely a statistical representation. In this paper, we present a Concept Vector Space Model (CVSM) representation which uses linguistic prior knowledge to capture the meanings of the documents. We also propose a linear kernel and a latent kernel for this space. The linear kernel takes advantage of the linguistic concepts whereas the latent kernel combines statistical and linguistic concepts. Indeed, the latter kernel uses latent concepts extracted by the Latent Semantic Analysis (LSA) in the CVSM. The kernels were evaluated on a text categorization task in the biomedical domain. The Ohsumed corpus, well known for being difficult to categorize, was used. The results have shown that the CVSM improves performance compared to the VSM.
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DIOŞAN, LAURA, ALEXANDRINA ROGOZAN, and JEAN-PIERRE PECUCHET. "LEARNING SVM WITH COMPLEX MULTIPLE KERNELS EVOLVED BY GENETIC PROGRAMMING." International Journal on Artificial Intelligence Tools 19, no. 05 (October 2010): 647–77. http://dx.doi.org/10.1142/s0218213010000352.
Повний текст джерелаАнотація:
Classic kernel-based classifiers use only a single kernel, but the real-world applications have emphasized the need to consider a combination of kernels — also known as a multiple kernel (MK) — in order to boost the classification accuracy by adapting better to the characteristics of the data. Our purpose is to automatically design a complex multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. In our model, each GP chromosome is a tree that encodes the mathematical expression of a multiple kernel. The evolutionary search process of the optimal MK is guided by the fitness function (or efficiency) of each possible MK. The complex multiple kernels which are evolved in this manner (eCMKs) are compared to several classic simple kernels (SKs), to a convex linear multiple kernel (cLMK) and to an evolutionary linear multiple kernel (eLMK) on several real-world data sets from UCI repository. The numerical experiments show that the SVM involving the evolutionary complex multiple kernels perform better than the classic simple kernels. Moreover, on the considered data sets, the new multiple kernels outperform both the cLMK and eLMK — linear multiple kernels. These results emphasize the fact that the SVM algorithm requires a combination of kernels more complex than a linear one in order to boost its performance.
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Segera, Davies, Mwangi Mbuthia, and Abraham Nyete. "Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis." BioMed Research International 2019 (December 16, 2019): 1–11. http://dx.doi.org/10.1155/2019/4085725.
Повний текст джерелаАнотація:
Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM). The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel. Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel. In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel). In comparison, two dual and two multiclass imbalanced standard microarray datasets were used. Experimental results in terms of three extended assessment metrics (F-score, G-mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models.
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Nehra, Rahul, and Kamalpreet Kaur. "AI-based Optimization of Tensile Strength of the Cement Concrete Incorporating Recycled Mixed Plastic Fine used in Road Construction." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 198–203. http://dx.doi.org/10.22214/ijraset.2023.56481.
Повний текст джерелаАнотація:
Abstract: One of the main problems in materials science and engineering is predicting the tensile strength of materials. In this study, we investigate how to model and forecast tensile strength (Tensile Strength in Mpa) based on different material attributes using Support Vector Regression (SVR) using Linear and Polynomial Kernels. The dataset includes the following details: plastic type, fine aggregate ratio, water/cement ratio, cement content, and associated tensile strength values. This work has two main goals: (1) to assess the predictive power of SVR models with various kernel functions and (2) to examine the significance of unique material attributes for prediction. To simulate the link between the input features and tensile strength, we used SVR in conjunction with a Linear Kernel. The final model included insightful information on how each feature affected the forecast. Our results show that the Polynomial Kernel SVR model may better reflect the complex interactions among the material attributes than the Linear Kernel SVR model, despite being more interpretable. Better prediction performance was offered by the Polynomial Kernel SVR, which also revealed the non-linear dependencies in the data.
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Andrade-Girón, Daniel, Edgardo Carreño-Cisneros, Cecilia Mejía-Dominguez, Julia Velásquez-Gamarra, William Marín-Rodriguez, Henry Villarreal-Torres, and Rosana Meleán-Romero. "Support vector machine with optimized parameters for the classification of patients with COVID-19." EAI Endorsed Transactions on Pervasive Health and Technology 9 (June 20, 2023): e8. http://dx.doi.org/10.4108/eetpht.9.3472.
Повний текст джерелаАнотація:
Introduction. The COVID-19 pandemic has had a significant impact worldwide, especially in health, where it is crucial to identify patients at high risk of clinical deterioration early.
Objective. This study aimed to design a model based on the support vector machine (SVM) algorithm, optimizing its parameters to classify patients with suspected COVID-19.
Methodology. One thousand patient records from two health establishments in Peru were used. After applying data preprocessing and variable engineering, the sample was reduced to 700 records. The construction of the model followed a machine learning methodology, using the linear, polynomial, sigmoid, and radial kernel functions, along with their estimated optimal parameters, to ensure the best performance.
Results. The results revealed that the SVM model with the linear and sigmoid kernels presented an accuracy of 95%, surpassing the polynomial kernel with 94% and the radial kernel (RBF) with 94%. In addition, a value of 0.92 was obtained for Cohen's kappa, which measures the degree of agreement between the predictions of the machine learning model and the actual results, which indicates an excellent deal for the linear and sigmoid kernel.
Conclusions. In conclusion, the SVM model with linear and sigmoid kernels could be a valuable tool for identifying patients at high risk of clinical deterioration in the context of the COVID-19 pandemic.
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Caraka, Rezzy Eko, Hasbi Yasin, and Adi Waridi Basyiruddin. "Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis." Jurnal Matematika 7, no. 1 (June 10, 2017): 43. http://dx.doi.org/10.24843/jmat.2017.v07.i01.p81.
Повний текст джерелаАнотація:
Recently, instead of selecting a kernel has been proposed which uses SVR, where the weight of each kernel is optimized during training. Along this line of research, many pioneering kernel learning algorithms have been proposed. The use of kernels provides a powerful and principled approach to modeling nonlinear patterns through linear patterns in a feature space. Another bene?t is that the design of kernels and linear methods can be decoupled, which greatly facilitates the modularity of machine learning methods. We perform experiments on real data sets crude palm oil prices for application and better illustration using kernel radial basis. We see that evaluation gives a good to fit prediction and actual also good values showing the validity and accuracy of the realized model based on MAPE and R2.
Keywords: Crude Palm Oil; Forecasting; SVR; Radial Basis; Kernel
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IVAN, KOMANG CANDRA, I. WAYAN SUMARJAYA, and MADE SUSILAWATI. "ANALISIS MODEL REGRESI NONPARAMETRIK SIRKULAR-LINEAR BERGANDA." E-Jurnal Matematika 5, no. 2 (May 31, 2016): 52. http://dx.doi.org/10.24843/mtk.2016.v05.i02.p121.
Повний текст джерелаАнотація:
Circular data are data which the value in form of vector is circular data. Statistic analysis that is used in analyzing circular data is circular statistics analysis. In regression analysis, if any of predictor or response variables or both are circular then the regression analysis used is called circular regression analysis. Observation data in circular statistic which use direction and time units usually don’t satisfy all of the parametric assumptions, thus making nonparametric regression as a good solution. Nonparametric regression function estimation is using epanechnikov kernel estimator for the linier variables and von Mises kernel estimator for the circular variable. This study showed that the result of circular analysis by using circular descriptive statistic is better than common statistic. Multiple circular-linier nonparametric regressions with Epanechnikov and von Mises kernel estimator didn’t create estimation model explicitly as parametric regression does, but create estimation from its observation knots instead.
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Sunitha, Lingam, and M. Bal Raju. "Multi-class classification for large datasets with optimized SVM by non-linear kernel function." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012015. http://dx.doi.org/10.1088/1742-6596/2089/1/012015.
Повний текст джерелаАнотація:
Abstract Most important part of Support Vector Machines(SVM) are the kernels. Although there are several widely used kernel functions, a carefully designed kernel will help improve the accuracy of SVM. The proposed work aims to develop a new kernel function for a multi-class support vector machine, perform experiments on various data sets, and compare them with other classification methods. Directly it is not possible multiclass classification with SVM. In this proposed work first designed a model for binary class then extended with the one-verses-all approach. Experimental results have proved the efficiency of the new kernel function. The proposed kernel reduces misclassification and time. Other classification methods observed better results for some data sets collected from the UCI repository.
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Jan, A. R. "An Asymptotic Model for Solving Mixed Integral Equation in Position and Time." Journal of Mathematics 2022 (August 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/8063971.
Повний текст джерелаАнотація:
In this paper, we considered a mixed integral equation (MIE) of the second kind in the space L 2 − b , b × C 0 , T , T < 1. The kernel of position has a singularity and takes some different famous forms, while the kernels of time are positive and continuous. Using an asymptotic method of separating the variables, we have a Fredholm integral equation (FIE) in position with variable parameters in time. Then, using the Toeplitz matrix method (TMM), we obtain a linear algebraic system (LAS) that can be solved numerically. Some applications with the aid of the maple 18 program are discussed when the kernel takes Coleman function, Cauchy kernel, Hilbert kernel, and a generalized logarithmic function. Also the error estimate, in each case, is computed.
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Lumbanraja, Favorisen Rossyking, Reza Aji Saputra, Kurnia Muludi, Astria Hijriani, and Akmal Junaidi. "IMPLEMENTASI SUPPORT VECTOR MACHINE DALAM MEMPREDIKSI HARGA RUMAH PADA PERUMAHAN DI KOTA BANDAR LAMPUNG." Jurnal Pepadun 2, no. 3 (December 1, 2021): 327–35. http://dx.doi.org/10.23960/pepadun.v2i3.90.
Повний текст джерелаАнотація:
Machine Learning has been widely used in terms of predictions for analyzing datasets. One method of Machine Learning is Support Vector Machine (SVM). The house has an important role in the survival of human beings. With the times, many developers are competing to build housing. The purpose of this study is to predicted the housing cost using Support Vector Machine. The data in this research used the data of house in Bandar lampung, the price, the location and the building specification. The amount of data used 51 datas and 33 variables with regression and classification, also used 3 kernels and it's model, 12 times first trial and next 6 experiments done with fitur selection. The trial result was kernel regression polynomial model reached the highest R 2 that was 95,99% linear kernel and gaussian kernel reached R 2 90,99% and 81,43% each. While in accuration classification model trial is obtained in 8 class of gaussian kernel as big as 91,18%, and linear kernel and polynimonal kernel get an accuracy of 90,20% and 89,90%.
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Ramadhan, Nur Ghaniaviyanto, Azka Khoirunnisa, Kurnianingsih Kurnianingsih, and Takako Hashimoto. "A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types." JOIV : International Journal on Informatics Visualization 7, no. 3 (September 10, 2023): 794. http://dx.doi.org/10.30630/joiv.7.3.1171.
Повний текст джерелаАнотація:
Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.
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Almaiah, Mohammed Amin, Omar Almomani, Adeeb Alsaaidah, Shaha Al-Otaibi, Nabeel Bani-Hani, Ahmad K. Al Hwaitat, Ali Al-Zahrani, Abdalwali Lutfi, Ali Bani Awad, and Theyazn H. H. Aldhyani. "Performance Investigation of Principal Component Analysis for Intrusion Detection System Using Different Support Vector Machine Kernels." Electronics 11, no. 21 (November 1, 2022): 3571. http://dx.doi.org/10.3390/electronics11213571.
Повний текст джерелаАнотація:
The growing number of security threats has prompted the use of a variety of security techniques. The most common security tools for identifying and tracking intruders across diverse network domains are intrusion detection systems. Machine Learning classifiers have begun to be used in the detection of threats, thus increasing the intrusion detection systems’ performance. In this paper, the investigation model for an intrusion detection systems model based on the Principal Component Analysis feature selection technique and a different Support Vector Machine kernels classifier is present. The impact of various kernel functions used in Support Vector Machines, namely linear, polynomial, Gaussian radial basis function, and Sigmoid, is investigated. The performance of the investigation model is measured in terms of detection accuracy, True Positive, True Negative, Precision, Sensitivity, and F-measure to choose an appropriate kernel function for the Support Vector Machine. The investigation model was examined and evaluated using the KDD Cup’99 and UNSW-NB15 datasets. The obtained results prove that the Gaussian radial basis function kernel is superior to the linear, polynomial, and sigmoid kernels in both used datasets. Obtained accuracy, Sensitivity, and, F-measure of the Gaussian radial basis function kernel for KDD CUP’99 were 99.11%, 98.97%, and 99.03%. for UNSW-NB15 datasets were 93.94%, 93.23%, and 94.44%.
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Manzan, Sebastiano, and Dawit Zerom. "Kernel estimation of a partially linear additive model." Statistics & Probability Letters 72, no. 4 (May 2005): 313–22. http://dx.doi.org/10.1016/j.spl.2005.02.005.
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Sheik Abdullah A., Akash K., Bhubesh K. R. A., and Selvakumar S. "Development of a Predictive Model for Textual Data Using Support Vector Machine Based on Diverse Kernel Functions Upon Sentiment Score Analysis." International Journal of Natural Computing Research 10, no. 2 (April 2021): 1–20. http://dx.doi.org/10.4018/ijncr.2021040101.
Повний текст джерелаАнотація:
This research work specifically focusses on the development of a predictive model for movie review data using support vector machine (SVM) classifier with its improvisations using different kernel functions upon sentiment score estimation. The predictive model development proceeds with user level data input with the data processing with the data stream for analysis. Then formal calculation of TF-IDF evaluation has been made upon data clustering using simple k-means algorithm. Once the labeled data has been sorted out, then the SVM with kernel functions corresponding to linear, sigmoid, rbf, and polynomial have been applied over the clustered data with specific parameter setting for each type of library functions. Performance of each of the kernels has been measured using precision, recall, and F-score values for each of the specified kernel, and from the analysis, it has been found that sentiment analysis using SVM linear kernel with sentiment score analysis has been found to provide an improved accuracy of about 91.18%.
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Chen, Kai, Rongchun Li, Yong Dou, Zhengfa Liang, and Qi Lv. "Ranking Support Vector Machine with Kernel Approximation." Computational Intelligence and Neuroscience 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4629534.
Повний текст джерелаАнотація:
Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.
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Kistler, Werner M., Wulfram Gerstner, and J. Leo van Hemmen. "Reduction of the Hodgkin-Huxley Equations to a Single-Variable Threshold Model." Neural Computation 9, no. 5 (July 1, 1997): 1015–45. http://dx.doi.org/10.1162/neco.1997.9.5.1015.
Повний текст джерелаАнотація:
It is generally believed that a neuron is a threshold element that fires when some variable u reaches a threshold. Here we pursue the question of whether this picture can be justified and study the four-dimensional neuron model of Hodgkin and Huxley as a concrete example. The model is approximated by a response kernel expansion in terms of a single variable, the membrane voltage. The first-order term is linear in the input and its kernel has the typical form of an elementary postsynaptic potential. Higher-order kernels take care of nonlinear interactions between input spikes. In contrast to the standard Volterra expansion, the kernels depend on the firing time of the most recent output spike. In particular, a zero-order kernel that describes the shape of the spike and the typical after-potential is included. Our model neuron fires if the membrane voltage, given by the truncated response kernel expansion, crosses a threshold. The threshold model is tested on a spike train generated by the Hodgkin-Huxley model with a stochastic input current. We find that the threshold model predicts 90 percent of the spikes correctly. Our results show that, to good approximation, the description of a neuron as a threshold element can indeed be justified.
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Amzile, Karim, and Mohamed Habachi. "Assessment of Support Vector Machine performance for default prediction and credit rating." Banks and Bank Systems 17, no. 1 (April 2, 2022): 161–75. http://dx.doi.org/10.21511/bbs.17(1).2022.14.
Повний текст джерелаАнотація:
Predicting the creditworthiness of bank customers is a major concern for banking institutions, as modeling the probability of default is a key focus of the Basel regulations. Practitioners propose different default modeling techniques such as linear discriminant analysis, logistic regression, Bayesian approach, and artificial intelligence techniques. The performance of the default prediction is evaluated by the Receiver Operating Characteristic (ROC) curve using three types of kernels, namely, the polynomial kernel, the linear kernel and the Gaussian kernel. To justify the performance of the model, the study compares the prediction of default by the support vector with the logistic regression using data from a portfolio of particular bank customers. The results of this study showed that the model based on the Support Vector Machine approach with the Radial Basis Function kernel, performs better in prediction, compared to the logistic regression model, with a value of the ROC curve equal to 98%, against 71.7% for the logistic regression model. Also, this paper presents the conception of a support vector machine-based rating tool designed to classify bank customers and determine their probability of default. This probability has been computed empirically and represents the proportion of defaulting customers in each class.
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Sun, Jian Ping, and Lin Tao Hu. "Application of Status Monitoring of Wind Turbines Based on Relevance Vector Machine Regression." Advanced Materials Research 347-353 (October 2011): 2337–41. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.2337.
Повний текст джерелаАнотація:
Based on the single kernel function relevance vector machine(RVM) models，a multiple load-forecasting model has been established and simulated with several compound kernel functions, including Gauss kernel, Laplace, linear compounded by Gauss and Laplace, Gauss and polynomial kernel. Each model gained comparatively reasonable results in simulation .Moreover, multi linear-compound kernel RVMs performed better than single kernel RVMs in terms of most evaluating indicators, which prove that RVM is an appropriate machine learning method in monitoring status of components of wind turbines.
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Pillonetto, Gianluigi, Tianshi Chen, and Lennart Ljung. "Kernel-based model order selection for linear system identification." IFAC Proceedings Volumes 46, no. 11 (2013): 257–62. http://dx.doi.org/10.3182/20130703-3-fr-4038.00043.
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Ma, Shujie, and Lijian Yang. "Spline-backfitted kernel smoothing of partially linear additive model." Journal of Statistical Planning and Inference 141, no. 1 (January 2011): 204–19. http://dx.doi.org/10.1016/j.jspi.2010.05.028.
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Arif, Osama H., and Omar Eidous. "Fourth-order kernel method for simple linear degradation model." Communications in Statistics - Simulation and Computation 47, no. 1 (October 18, 2017): 16–29. http://dx.doi.org/10.1080/03610918.2016.1186183.
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Purbaya, Muhammad Eka, Diovianto Putra Rakhmadani, Maliana Puspa Arum, and Luthfi Zian Nasifah. "Implementation of n-gram Methodology to Analyze Sentiment Reviews for Indonesian Chips Purchases in Shopee E-Marketplace." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 3 (June 2, 2023): 609–17. http://dx.doi.org/10.29207/resti.v7i3.4726.
Повний текст джерелаАнотація:
Chips are a well-known product among Small and Medium Enterprises (SMEs). In order to enhance the quality of chips as an SME product, sentiment analysis is a crucial step. In this research, sentiment analysis of chip purchases on the Shopee E-marketplace was conducted using the Natural Language Processing (NLP) method, utilizing the N-Gram Model and Term Frequent-Inverse Document Frequency (TF-IDF) as feature extraction techniques, and the Support Vector Machine (SVM) algorithm for sentiment classification. The objective of this research is to identify the most suitable feature extraction model and optimal SVM kernel type from the options of Linear, Polynomial degree, Gaussian RBF, and Sigmoid kernels. Results from the experiments indicate that the TF-IDF and unigram feature extraction techniques offer the best performance for SVM classification when utilizing the Linear kernel. By labeling the dataset, it was observed that using a lexicon-based approach for sentiment classification resulted in 84.31% of the total reviews being positive. The words "price", "cheap" and "quality" in unigram have the highest weights above 0.040. In the unigram model, linear kernel accuracy and precision performance values are 88.4% and 87.3%. At the same time, the recall performance values is 88.4%. The results of the F1-Score assessment matrix from Unigram were 86.9%, Bigram was 78.5% and Trigram was 77.4%. Ultimately, the unigram model combined with a linear kernel in the SVM algorithm demonstrates strong potential for application in the development of various systems focused on detecting user reviews in the Indonesian language on the Shopee E-Marketplace.
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Puspitasari, Chasandra, Nur Rokhman, and Wahyono. "PREDICTION OF OZONE (O3) VALUES USING SUPPORT VECTOR REGRESSION METHOD." Jurnal Informatika Polinema 7, no. 4 (August 31, 2021): 81–88. http://dx.doi.org/10.33795/jip.v7i4.777.
Повний текст джерелаАнотація:
A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.
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TANG, Guoxin, Lang YU, Wangyong LV, and Yuhuai SUN. "Dual-kernel echo state network for nonlinear time series prediction." Proceedings of the Romanian Academy, Series A: Mathematics, Physics, Technical Sciences, Information Science 24, no. 2 (June 28, 2023): 179–90. http://dx.doi.org/10.59277/pra-ser.a.24.2.10.
Повний текст джерелаАнотація:
An echo state network (ESN) is a recurrent neural network (RNN) often applied to nonlinear time series prediction. The traditional ESN randomly generates the weights of the input layer and the reservoir layer and does not change these weights, and generally only learns the weights of the output layer through linear regression, so the training speed is very fast. In this work, we propose a novel kernel echo state network (KESN). In KESN, the random weights of the input layer are removed and a series of gaussian kernels are used to replace the neurons in the input layer. Similar to radial basis function (RBF) neural networks, KESN can use the k-means algorithm to generate the kernel center and estimate the bandwidth of the kernel function. We prove that a KESN has echo state property, which is an important factor of KESN that can normally work. Furthermore, kernel ridge regression (KRR) is used to learn the weights of the output layer instead of a simple linear model. Finally, to obtain the optimal parameters of the model, the tree-structured parzen estimator approach (TPE) is used to optimize the hyperparameters of the model. In a time series prediction experiment, it is proved that KESN is more stable and performs better than the echo state network which randomly generates weights and trains output weights using linear models. We found that the reservoir layer weights are equivalent to a dropout operation, and the KESN is inherently equivalent to a regularized neural network. We call the KRR-based KESN dual-kernel echo state network (DKESN).
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Yundari, Yundari, and Setyo Wira Rizki. "Invertibility of Generalized Space-Time Autoregressive Model with Random Weight." CAUCHY 6, no. 4 (May 30, 2021): 246–59. http://dx.doi.org/10.18860/ca.v6i4.11254.
Повний текст джерелаАнотація:
The generalized linear process accomplishes stationarity and invertibility properties. The invertibility property must be having a series of convergence conditions of the process parameter. The generalized Space-Time Autoregressive (GSTAR) model is one of the stationary linear models therefore it is necessary to reveal the invertibility through the convergence of the parameter series. This article studies the invertibility of model GSTAR(1;1) with kernel random weight. The result shows that the model GSTAR(1;1) under kernel random weight fulfills the invertibility property and obtains a finite order of Generalized Space-Time Moving Average (GSTMA) process. The other result obtained is the time order of the finite orde . On the Triangular kernel resulted in the relatively great value n, so that it does not apply to the kernel with a finite value n.
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Böhm, Volker, and Jan Wenzelburger. "PERFECT PREDICTIONS IN ECONOMIC DYNAMICAL SYSTEMS WITH RANDOM PERTURBATIONS." Macroeconomic Dynamics 6, no. 5 (September 26, 2002): 687–712. http://dx.doi.org/10.1017/s1365100501010136.
Повний текст джерелаАнотація:
The paper studies multivariate non linear economic dynamical systems with an expectations feedback subjected to exogenous perturbations. In these systems, agents form expectations on future variables based on subjective transition probabilities given by a Markov kernel. The notion of a perfect Markov kernel that generates rational expectations along all orbits of the system is proposed. Conditions are provided under which perfect Markov kernels exist. Applications are given to models of the Cobweb type, to multivariate affine-linear systems, and to the stochastic OLG model of economic growth. For the latter two models, it is shown when a globally attracting random fixed point with rational expectations exists.
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Mei Serin Sitio, Claudia, Yuliant Sibaroni, and Sri Suryani Prasetiyowati. "IDENTIFYING POSSIBLE RUMOR SPREADERS ON TWITTER USING THE SVM AND FEATURE LEVEL EXTRACTION." Jurnal Teknik Informatika (Jutif) 4, no. 3 (June 26, 2023): 611–18. http://dx.doi.org/10.52436/1.jutif.2023.4.3.868.
Повний текст джерелаАнотація:
In everyday life, many events occur and give rise to various kinds of information, which are also rumors. Rumors can cause fear and influence public opinion about the event in question. Identifying possible rumor spreaders is extremely helpful in preventing the spread of rumors. Feature extraction can be done to expand the feature set, which consists of conversational features in the form of social networks formed from user replies, user features such as following, tweet count, verified, etc., and tweet features with text analysis such as punctuation and sentiment values. These features become instances used for classification. This study aims to identify possible spreaders of rumors on Twitter with the SVM classification model. This instance-based classification algorithm is good for linear and non-linear classification. In the non-linear classification, additional kernels are used, such as linear, RBF, and sigmoid. The research focuses on getting the best model with high performance values from all the models and kernel functions that have been defined. It was found that the SVM classification model with the RBF kernel has a high overall performance value for each data combination with a ratio of the amount of data is 1:1 or the difference is very large. This model gives accurate results with an average of 97.02%. With a wide distribution of data, the SVM classification model with the RBF kernel is able to map the data properly.
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Ma, Xiaoyan, Yanbin Zhang, Hui Cao, Shiliang Zhang, and Yan Zhou. "Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis." Journal of Spectroscopy 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/2689750.
Повний текст джерелаАнотація:
Accurate and fast determination of blood component concentration is very essential for the efficient diagnosis of patients. This paper proposes a nonlinear regression method with high-dimensional space mapping for blood component spectral quantitative analysis. Kernels are introduced to map the input data into high-dimensional space for nonlinear regression. As the most famous kernel, Gaussian kernel is usually adopted by researchers. More kernels need to be studied for each kernel describes its own high-dimensional feature space mapping which affects regression performance. In this paper, eight kernels are used to discuss the influence of different space mapping to the blood component spectral quantitative analysis. Each kernel and corresponding parameters are assessed to build the optimal regression model. The proposed method is conducted on a real blood spectral data obtained from the uric acid determination. Results verify that the prediction errors of proposed models are more precise than the ones obtained by linear models. Support vector regression (SVR) provides better performance than partial least square (PLS) when combined with kernels. The local kernels are recommended according to the blood spectral data features. SVR with inverse multiquadric kernel has the best predictive performance that can be used for blood component spectral quantitative analysis.
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Hamzah, Muhammad Amir, and Siti Hajar Othman. "Performance Evaluation of Support Vector Machine Kernels in Intrusion Detection System for Wireless Sensor Network." International Journal of Innovative Computing 12, no. 1 (November 16, 2021): 9–15. http://dx.doi.org/10.11113/ijic.v12n1.334.
Повний текст джерелаАнотація:
Wireless sensor network is very popular in the industrial application due to its characteristics of infrastructure-less wireless network and self-configured for physical and environmental conditions monitoring. However, the dynamic environments of wireless network expose WSN to network vulnerabilities. Intrusion Detection System (IDS) has been used to mitigate the vulnerability issue of network. Researches towards the efficiency improvement of WSN-IDS has been extensively done because the rapid growth of technologies influence the growth of network attacks. Implementation Support Vector Machine (SVM) was found to be one of the optimum algorithms for the improvement of WSN-IDS. Yet, classification efficiency of SVM is based on the kernel function used because different kernel gives different SVM architecture. Linear classification of SVM has limitation to maximize the margin due to the dynamic environment of wireless network which consist of nonlinear data. Since maximizing the margin is the primary goal of SVM, it is crucial to implement the optimum kernel in the classification of nonlinear data. Each SVM model in this research use different kernels which are Linear, RBF, Polynomial and Sigmoid kernels. Further, NSL-KDD dataset was used for the experiment of this research. Performance of each kernel were evaluated based on the experimental result obtained and it was found that RBF kernel provides the best classification accuracy with the score of 91%. Finally, discussion based on the findings was made.
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Jerop, Brenda, and Davies Rene Segera. "An Efficient PCA-GA-HKSVM-Based Disease Diagnostic Assistant." BioMed Research International 2021 (October 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/4784057.
Повний текст джерелаАнотація:
Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a disease. To improve on the performance of these techniques, this paper presents a technique which involves feature selection using principal component analysis (PCA), a hybrid kernel-based support vector machine (HKSVM) classification model and hyperparameter optimization using genetic algorithm (GA). The HKSVM in this paper introduces a new way of combining three kernels: Radial basis function (RBF), linear, and polynomial. Combining local (RBF) and global (linear and polynomial) kernels has the effect of improved model performance. This is because the local kernels are better able to distinguish points closer to each other while the global kernels are more suited to distinguish points that are far away from each other. The PCA-GA-HKSVM is used on 7 different medical datasets, with two datasets being multiclass datasets and 5 datasets being binary. Performance evaluation metrics used were accuracy, precision, and recall. It was observed that the PCA-GA-HKSVM offered better performance than the single kernel support vector machines (SVMs).
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Shen, Xiangjun, Kou Lu, Sumet Mehta, Jianming Zhang, Weifeng Liu, Jianping Fan, and Zhengjun Zha. "MKEL: Multiple Kernel Ensemble Learning via Unified Ensemble Loss for Image Classification." ACM Transactions on Intelligent Systems and Technology 12, no. 4 (June 6, 2021): 1–21. http://dx.doi.org/10.1145/3457217.
Повний текст джерелаАнотація:
In this article, a novel ensemble model, called Multiple Kernel Ensemble Learning (MKEL), is developed by introducing a unified ensemble loss. Different from the previous multiple kernel learning (MKL) methods, which attempt to seek a linear combination of basis kernels as a unified kernel, our MKEL model aims to find multiple solutions in corresponding Reproducing Kernel Hilbert Spaces (RKHSs) simultaneously. To achieve this goal, multiple individual kernel losses are integrated into a unified ensemble loss. Therefore, each model can co-optimize to learn its optimal parameters by minimizing a unified ensemble loss in multiple RKHSs. Furthermore, we apply our proposed ensemble loss into the deep network paradigm and take the sub-network as a kernel mapping from the original input space into a feature space, named Deep-MKEL (D-MKEL). Our D-MKEL model can utilize the diversified deep individual sub-networks into a whole unified network to improve the classification performance. With this unified loss design, our D-MKEL model can make our network much wider than other traditional deep kernel networks and more parameters are learned and optimized. Experimental results on several mediate UCI classification and computer vision datasets demonstrate that our MKEL model can achieve the best classification performance among comparative MKL methods, such as Simple MKL, GMKL, Spicy MKL, and Matrix-Regularized MKL. On the contrary, experimental results on large-scale CIFAR-10 and SVHN datasets concretely show the advantages and potentialities of the proposed D-MKEL approach compared to state-of-the-art deep kernel methods.
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Azmi Verdikha, Naufal, Reza Habid, and Asslia Johar Latipah. "Analisis DistilBERT dengan Support Vector Machine (SVM) untuk Klasifikasi Ujaran Kebencian pada Sosial Media Twitter." METIK JURNAL 7, no. 2 (December 30, 2023): 101–10. http://dx.doi.org/10.47002/metik.v7i2.583.
Повний текст джерелаАнотація:
Hate speech is a significant issue in content management on social media platforms. Effective classification of hate speech plays a crucial role in maintaining a safe social media environment, combating discrimination, and protecting users. This study evaluates a hate speech classification model using SVM with linear and polynomial kernels. The dataset used consists of labeled Indonesian-language tweets. The importance of developing an effective classification model to address hate speech has led to the utilization of DistilBERT as a feature extraction method. However, DistilBERT has high-dimensional features, necessitating dimensionality reduction to reduce model complexity. Therefore, in this study, the PCA dimensionality reduction method is implemented with various scenarios of dimensionality, namely 10, 20, 30, 40, and 50. Evaluation is performed using F1-Score, and the entire study is evaluated using 10-fold cross-validation. The evaluation results indicate that in the scenario with a linear kernel, the model achieves the highest F1-Score of 0.75 in the 50-dimensional scenario. Meanwhile, in the scenario with a polynomial kernel, the model achieves the highest F1-Score of 0.7857 in the 50-dimensional scenario. These findings demonstrate that the use of a polynomial kernel with 50 dimensions yields the best performance in classifying hate speech.
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Smith, Christopher J., Ryan J. Kramer, and Adriana Sima. "The HadGEM3-GA7.1 radiative kernel: the importance of a well-resolved stratosphere." Earth System Science Data 12, no. 3 (September 13, 2020): 2157–68. http://dx.doi.org/10.5194/essd-12-2157-2020.
Повний текст джерелаАнотація:
Abstract. We present top-of-atmosphere and surface radiative kernels based on the atmospheric component (GA7.1) of the HadGEM3 general circulation model developed by the UK Met Office. We show that the utility of radiative kernels for forcing adjustments in idealised CO2 perturbation experiments is greatest where there is sufficiently high resolution in the stratosphere in both the target climate model and the radiative kernel. This is because stratospheric cooling to a CO2 perturbation continues to increase with height, and low-resolution or low-top kernels or climate model output are unable to fully resolve the full stratospheric temperature adjustment. In the sixth phase of the Coupled Model Intercomparison Project (CMIP6), standard atmospheric model data are available up to 1 hPa on 19 pressure levels, which is a substantial advantage compared to CMIP5. We show in the IPSL-CM6A-LR model where a full set of climate diagnostics are available that the HadGEM3-GA7.1 kernel exhibits linear behaviour and the residual error term is small, as well as from a survey of kernels available in the literature that in general low-top radiative kernels underestimate the stratospheric temperature response. The HadGEM3-GA7.1 radiative kernels are available at https://doi.org/10.5281/zenodo.3594673 (Smith, 2019).
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Sadewo, Wismaji, Zuherman Rustam, Hamidah Hamidah, and Alifah Roudhoh Chusmarsyah. "Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel." Symmetry 12, no. 4 (April 23, 2020): 667. http://dx.doi.org/10.3390/sym12040667.
Повний текст джерелаАнотація:
Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.
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Papaioannou, Athanasios, and Stefanos Zafeiriou. "Principal Component Analysis With Complex Kernel: The Widely Linear Model." IEEE Transactions on Neural Networks and Learning Systems 25, no. 9 (September 2014): 1719–26. http://dx.doi.org/10.1109/tnnls.2013.2285783.
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Jirsa, Ondřej, and Ivana Polišenská. "Identification of Fusarium damaged wheat kernels using image analysis." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59, no. 5 (2011): 125–30. http://dx.doi.org/10.11118/actaun201159050125.
Повний текст джерелаАнотація:
Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON) content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue), very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance). The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.
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Chen, Yu, Xin Ling Wen, and Jin Tao Meng. "Research of Volterra Series Kernel Coefficient Calculation Method." Advanced Materials Research 255-260 (May 2011): 2967–71. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2967.
Повний текст джерелаАнотація:
Volterra series kernel coefficient calculation of non-linear system is a difficult problem. In this paper, we introduce some way, which can get kernel coefficient. With the increasing of memory length and identification order, it can make calculation complex and hard to rebuild system non-linear model. This article introduces some conventional Volterra series kernel calculation ways, and introduces a kind of method to get Volterra series kernel through using Hilbert space method emphasis, this method can transform Volterra series kernel coefficient calculation problem into reproducing kernel coefficient problem, which has largely simple calculation rate and can get any order Volterra series kernel coefficient in theory.
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Handayani, Meli, Rika Rosnelly, and Hartono Hartono. "Classification of Basurek Batik Using Pre-Trained VGG-16 and Support Vector Machine." International Conference on Information Science and Technology Innovation (ICoSTEC) 2, no. 1 (March 5, 2023): 40–44. http://dx.doi.org/10.35842/icostec.v2i1.34.
Повний текст джерелаАнотація:
By introducing Indonesian batik motifs, we know that the island of Sumatra, especially Bengkulu and Jambi provinces, has a distinctive batik called Basurek batik. This research aims to classify the two batik motifs using the Support Vector Machine (SVM) algorithm. First, we extract the image of the batik motif with a pre trained VGG-16 model and then use them as a dataset for the SVM classification process. The classification process itself uses linear, polynomial, and sigmoid kernels. We divided the data 90:10 and used 10-fold cross-validation to analyze each training and testing data classification result. The results of this study are the highest values of accuracy, precision, and recall of 76.4%, 76.5%, and 76.4% produced by the linear kernel for the training data classification. For the testing data classification, both the linear and polynomial kernels generate the best accuracy, precision, and recall values of 87.5%, 90%, and 85.5%. On average, incorporating the training and testing classification results, we found that the linear kernel is the best function for classifying the Basurek batik motif using the collected images from the internet.
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Hall, Peter, and Joel L. Horowitz. "Bandwidth Selection in Semiparametric Estimation of Censored Linear Regression Models." Econometric Theory 6, no. 2 (June 1990): 123–50. http://dx.doi.org/10.1017/s0266466600005089.
Повний текст джерелаАнотація:
Quantile and semiparametric M estimation are methods for estimating a censored linear regression model without assuming that the distribution of the random component of the model belongs to a known parametric family. Both methods require estimating derivatives of the unknown cumulative distribution function of the random component. The derivatives can be estimated consistently using kernel estimators in the case of quantile estimation and finite difference quotients in the case of semiparametric M estimation. However, the resulting estimates of derivatives, as well as parameter estimates and inferences that depend on the derivatives, can be highly sensitive to the choice of the kernel and finite difference bandwidths. This paper discusses the theory of asymptotically optimal bandwidths for kernel and difference quotient estimation of the derivatives required for quantile and semiparametric M estimation, respectively. We do not present a fully automatic method for bandwidth selection.
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Verdiansyah, Muhammad Arik, and Suwanda. "Penerapan Metode Regresi Komponen Utama Kernel untuk Prediksi Harga Rumah." Bandung Conference Series: Statistics 3, no. 2 (August 2, 2023): 653–61. http://dx.doi.org/10.29313/bcss.v3i2.9084.
Повний текст джерелаАнотація:
Abstract. Linear regression analysis is a statistical method used to model the relationship between the dependent variable and one or more independent variables expressed in the form of a regression equation. The Ordinary Least Square (OLS) can be used to estimate regression parameters. The regression parameter estimator obtained will be good if the assumptions that apply to MKT are met. One of the assumptions that must be met is that there is a linear relationship between the dependent variable and each independent variable. If linearity is violated, kernels can be used. In addition, another assumption that must be met is the absence of multicollinearity between independent variables. An alternative method that can be used to overcome multicollinearity is using Principal Component Analysis. If the linearity and multicollinearity assumptions are violated, the Kernel Principal Component Regression method can be applied. This study will apply the Kernel Principal Component Regression (KPCR) method, where the principal component is a nonlinear combination of independent variables through a kernel function. This KPCR method is implemented to predict house prices in South Jakarta based on building area, land area, number of bedrooms, number of bathrooms, and number of car capacity in the garage. Based on the results of the study, 3 main components of the kernel were formed with a prediction model Y ̂ = 76,3612 + 409,5373 KUK1 + 328,4967 KUK2 – 291,7757 KUK3 and a coefficient of determination (R2) of 70.4640%.
Abstrak. Analisis regresi linear merupakan suatu metode statistika yang digunakan untuk memodelkan hubungan antara variabel dependen dengan satu atau lebih variabel independen yang dinyatakan dalam bentuk persamaan regresi. Dalam menaksir parameter regresi dapat menggunakan Metode Kuadrat Terkecil (MKT). Penaksir parameter regresi yang diperoleh akan baik jika asumsi-asumsi yang berlaku untuk MKT dipenuhi. Salah satu asumsi yang harus dipenuhi adalah terdapat hubungan linear antara variabel dependen dan masing-masing variabel independen. Apabila linearitas terlanggar, dapat menggunakan kernel. Selain itu, asumsi lainnya yang harus terpenuhi adalah tidak adanya multikolinearitas antar variabel independen. Metode alternatif yang dapat digunakaan untuk mengatasi multikolinearitas adalah menggunakan Analisis Komponen Utama. Apabila asumsi linearitas dan multikolinearitas terlanggar, dapat diterapkan metode Regresi Komponen Utama Kernel. Dalam penelitian ini, akan diterapkan metode Regresi Komponen Utama Kernel (RKUK), dimana komponen utama merupakan kombinasi nonlinier dari variabel independen melalui fungsi kernel. Metode RKUK ini diimplementasikan untuk memprediksi harga rumah di Jakarta Selatan berdasarkan luas bangunan, luas tanah, jumlah kamar tidur, jumlah kamar mandi, dan jumlah kapasitas mobil dalam garasi. Berdasarkan hasil penelitian, terbentuk 3 buah komponen utama kernel dengan model prediksi Y ̂ = 76,3612 + 409,5373 KUK1 + 328,4967 KUK2 – 291,7757 KUK3 serta nilai koefisien determinasi (R2) sebesar 70,4640%.
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Verma, Neetu, Sujoy Das, and Namita Srivastava. "Multiple kernel support vector regression for pricing nifty option." International Journal of Applied Mathematical Research 4, no. 4 (September 29, 2015): 488. http://dx.doi.org/10.14419/ijamr.v4i4.5023.
Повний текст джерелаАнотація:
<p>The goal of present experiments is to investigate the use of multiple kernel learning as a tool for pricing options in the context of Indian stock market for Nifty index options. In this paper, fair price of an option is predicted by Multiple Kernel Support Vector Regression (MKLSVR) using linear combinations of kernels and Single Kernel Support Vector Regression (SKSVR). Prices of option highly depend on different money market conditions like deep-in-the-money, in-the-money, at-the-money, out-of-money and deep-out-of-money condition. The experimental study attempts to identify the forecasting errors with the help of mean square error; root meant square error, and normalized root meant square error between the market option prices and the calculated option prices by model for all market conditions. The results reflect that multiple kernel support vector regression performed fairly well in comparison to support vector regression with single kernel.</p>
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James Momoh, Omeiza, and Vincent Nwoya Okafor. "Mathematical Modeling of the Solvent Extraction of Palm Kernel Oil from Palm Kernel." مجلة جامعة فلسطين التقنية للأبحاث 3, no. 1 (February 10, 2015): 23–29. http://dx.doi.org/10.53671/pturj.v3i1.36.
Повний текст джерелаАнотація:
The model for the solvent extraction of palm kernel oil from palm kernel was generated for the process at varying particle sizes of palm kernel, temperature of extraction, duration of extraction and mass of palm kernel respectively using Least Square Linear Equation. Petroleum ether was used as solvent to carry out the extraction in a soxhlet apparatus. The percentage oil yield was determined for every extraction carried out. The experimental results obtained showed that percentage oil yield decreases with increase in particle size and mass, but increases with increase in the temperature and duration of extraction. The characterization of the extracted oil was also done to determine its physiochemical properties, which revealed palm kernel oil as a non-drying oil. Statistical analyses of each variable studied and its corresponding oil yield was carried out followed by the modeling of the extraction process for each parameter using least square linear equation. The interpretation of the model developed revealed a model which was significant in the variations obtained from the experimental results.
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James Momoh, Omeiza, and Vincent Nwoya Okafor. "Mathematical Modeling of the Solvent Extraction of Palm Kernel Oil from Palm Kernel." مجلة جامعة فلسطين التقنية خضوري للأبحاث 3, no. 1 (February 10, 2015): 23–29. http://dx.doi.org/10.53671/ptukrj.v3i1.36.
Повний текст джерелаАнотація:
The model for the solvent extraction of palm kernel oil from palm kernel was generated for the process at varying particle sizes of palm kernel, temperature of extraction, duration of extraction and mass of palm kernel respectively using Least Square Linear Equation. Petroleum ether was used as solvent to carry out the extraction in a soxhlet apparatus. The percentage oil yield was determined for every extraction carried out. The experimental results obtained showed that percentage oil yield decreases with increase in particle size and mass, but increases with increase in the temperature and duration of extraction. The characterization of the extracted oil was also done to determine its physiochemical properties, which revealed palm kernel oil as a non-drying oil. Statistical analyses of each variable studied and its corresponding oil yield was carried out followed by the modeling of the extraction process for each parameter using least square linear equation. The interpretation of the model developed revealed a model which was significant in the variations obtained from the experimental results.
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Boac, Josephine M., Mark E. Casada, Lester O. Pordesimo, Frank H. Arthur, Ronaldo G. Maghirang, and Christian D. Mina. "Effect of Internal Insect Infestation on Single Kernel Mass and Particle Density of Corn and Wheat." Applied Engineering in Agriculture 38, no. 3 (2022): 583–88. http://dx.doi.org/10.13031/aea.14858.
Повний текст джерелаАнотація:
HighlightsSingle kernel mass and particle density were not significantly affected by the number of rice weevils feeding within a corn kernel and lesser grain borers feeding within a wheat kernel.In both corn and wheat, single kernel mass decreased after the larval stage of internally feeding insects.Particle density increased linearly with insect age for both rice weevils in corn and lesser grain borer in wheat.The increasing particle density while the kernel mass was being eroded indicates that the kernel internal void was detected by the gas pycnometer employed for measurement of the true volume of grain kernels.Abstract. To model the dynamics of insect infestation in a grain handling system using the discrete element method (DEM), physical properties of the infested kernels compared to their sound counterparts are needed, specifically particle density and single kernel mass of infested kernels. Thus, the objective of this study was to determine the particle density and single kernel mass of internally infested kernels as affected by insect age. Corn and wheat were infested with internal feeders: rice weevil (RW), Sitophilus oryzae (L.), in corn and lesser grain borer (LGB), Rhyzopertha dominica (F.), in wheat. The internal feeders were allowed to grow and mature inside the kernels and properties were measured for representative samples selected using X-ray imaging approximately 14, 28, 35, and 42 days after the end of a 4-day oviposition period. The measured kernel physical properties were not affected by the number of internal insects per kernel. In both corn and wheat, single kernel mass decreased after the larval stage of internally feeding insects. Single kernel mass decreased from 374 mg in sound corn to 346 mg in corn with pre-emerged RW adults and from 31.4 mg in sound wheat to 25.9 mg in wheat with pre-emerged LGB adults. Particle density increased with insect age for both RW in corn and LGB in wheat with a linear trend. The increasing particle density while the kernel mass eroded indicates that kernel internal void was detected by the gas pycnometer employed for measurement of the true volume of grain kernels. Data obtained from this study enables effective DEM modeling of grain commingling of insect-infested and sound grain kernels in grain handling systems. Keywords: Corn, Insect age, Internal feeders, Insect infestation, Lesser grain borer, Particle density, Rice weevil, Single kernel mass, Wheat.
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Febrian Sengkey, Daniel, Agustinus Jacobus, and Fabian Johanes Manoppo. "Effects of kernels and the proportion of training data on the accuracy of SVM sentiment analysis in lecturer evaluation." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 734. http://dx.doi.org/10.11591/ijai.v9.i4.pp734-743.
Повний текст джерелаАнотація:
Support vector machine (SVM) is a known method for supervised learning in sentiment analysis and there are many studies about the use of SVM in classifying the sentiments in lecturer evaluation. SVM has various parameters that can be tuned and kernels that can be chosen to improve the classifier accuracy. However, not all options have been explored. Therefore, in this study we compared the four SVM kernels: radial, linear, polynomial, and sigmoid, to discover how each kernel influences the accuracy of the classifier. To make a proper assessment, we used our labeled dataset of students’ evaluations toward the lecturer. The dataset was split, one for training the classifier, and another one for testing the model. As an addition, we also used several different ratios of the training:testing dataset. The split ratios are 0.5 to 0.95, with the increment factor of 0.05. The dataset was split randomly, hence the splitting-training-testing processes were repeated 1,000 times for each kernel and splitting ratio. Therefore, at the end of the experiment, we got 40,000 accuracy data. Later, we applied statistical methods to see whether the differences are significant. Based on the statistical test, we found that in this particular case, the linear kernel significantly has higher accuracy compared to the other kernels. However, there is a tradeoff, where the results are getting more varied with a higher proportion of data used for training.
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Kundu, Anupam. "Non-local linear response in anomalous transport." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 11 (November 1, 2023): 113204. http://dx.doi.org/10.1088/1742-5468/ad0637.
Повний текст джерелаАнотація:
Abstract The anomalous heat transport observed in low-dimensional classical systems is associated with super-diffusive spreading of the space–time correlation of the conserved fields in the system. This leads to a non-local linear response relation between the heat current and the local temperature gradient in the non-equilibrium steady state. This relation provides a generalization of Fourier’s law of heat transfer and is characterized by a non-local kernel operator related to the fractional operators describing super-diffusion. The kernel is essentially proportional, in an appropriate hydrodynamic scaling limit, to the time integral of the space–time correlations of local currents in equilibrium. In finite-size systems, the time integral of correlation of microscopic currents at different locations over an infinite duration is independent of the locations. On the other hand, the kernel operator is space-dependent. We demonstrate that the resolution of this apparent puzzle becomes evident when we consider an appropriate combination of the limits of a large system size and a long integration time. Our study shows the importance of properly handling these limits, even when dealing with (open) systems connected to reservoirs. In particular, we reveal how to extract the kernel operator from simulated microscopic current–current correlation data. For two model systems exhibiting anomalous transport, we provide a direct and detailed numerical verifications of the kernel operators.
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Chiuso, A., and G. Pillonetto. "System Identification: A Machine Learning Perspective." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 281–304. http://dx.doi.org/10.1146/annurev-control-053018-023744.
Повний текст джерелаАнотація:
Estimation of functions from sparse and noisy data is a central theme in machine learning. In the last few years, many algorithms have been developed that exploit Tikhonov regularization theory and reproducing kernel Hilbert spaces. These are the so-called kernel-based methods, which include powerful approaches like regularization networks, support vector machines, and Gaussian regression. Recently, these techniques have also gained popularity in the system identification community. In both linear and nonlinear settings, kernels that incorporate information on dynamic systems, such as the smoothness and stability of the input–output map, can challenge consolidated approaches based on parametric model structures. In the classical parametric setting, the complexity of the model (the model order) needs to be chosen, typically from a finite family of alternatives, by trading bias and variance. This (discrete) model order selection step may be critical, especially when the true model does not belong to the model class. In regularization-based approaches, model complexity is controlled by tuning (continuous) regularization parameters, making the model selection step more robust. In this article, we review these new kernel-based system identification approaches and discuss extensions based on nuclear and [Formula: see text] norms.
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Lv, Ning, Guang Yuan Bai, Lu Qi Yan, and Yuan Jian Fu. "The Fault Diagnosis Model of Beer Fermentation Process Based on Kernel Principal Component Analysis for Constant Value Detection." Advanced Materials Research 1030-1032 (September 2014): 1822–27. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1822.
Повний текст джерелаАнотація:
In order to overcome the application limitations of principal component analysis fault diagnose model in non-linear time-varying and reduce computational complexity for process monitoring based on non-linear principal component, we introduced kernel transformation theory of nonlinear space to extract data feature extraction and a fault monitoring model based on kernel principal component analysis (KPCA) for constant value detection was proposed. Through the proper selection of kernel function parameter values, the KPCA model can achieve constant value of process fault detection and has lower computational complexity than other non-linear algorithms. The fault detection experiment for beer fermentation process shows that this method is able to detect process faults in a timely manner and has good real-time performance and accuracy in the batch process of slowly time-varying.
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Giannattasio, Pietro, Marco Pretto, and Enrico De Betta. "A phenomenological model for predicting the early development of the flame kernel in spark-ignition engines." Journal of Physics: Conference Series 2648, no. 1 (December 1, 2023): 012070. http://dx.doi.org/10.1088/1742-6596/2648/1/012070.
Повний текст джерелаАнотація:
Abstract This work presents a simple and effective phenomenological model for the prediction of the early growth of the flame kernel in SI engines, including its initiation as a result of the electrical breakdown of the fuel/air mixture between the spark plug electrodes. The present model aims to provide an improved description of the ignition-affected early phases of flame kernel development compared to the majority of models currently available in literature. In particular, these models focus on electrical energy supply and turbulence, whereas the stretch-induced kernel growth slowdown is quantified with linear models that are inconsistent with the small kernel radius. For the flame kernel initiation, this model replaces the current methods that rely on 1D heat diffusion within a plasma column with a more consistent analysis of post-breakdown conditions. Concerning the kernel growth, the present model couples the mass and energy conservation equations of a spherical kernel with the species and temperature profiles outside of it. This combination leads to a non-linear description of the flame stretch, according to which the kernel development is controlled by the Lewis-number-dependent balance between the heat gained via combustion and the heat lost via thermal diffusion. As a result, the kernel temperature differs from the adiabatic flame temperature, causing the laminar flame speed to change from its adiabatic value and ultimately affecting the overall kernel development. Kernel growth predictions are conducted for laminar flames and compared to literature data, showing a satisfactory agreement and highlighting the ability to describe the stretch-induced kernel slowdown, up to its possible extinction. A good agreement with literature data is also obtained for kernel expansions under moderately turbulent conditions, typical of internal combustion engines. The simple formulation of the present model enables swift integration into phenomenological combustion models for sparkignition engines, while simultaneously offering useful insight into the early kernel development even for CFD-based approaches.
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Koesmarno, H. K., and J. R. Sedcole. "A method for the analysis of barley kernel growth data from designed experiments." Journal of Agricultural Science 123, no. 1 (August 1994): 25–33. http://dx.doi.org/10.1017/s0021859600067733.
Повний текст джерелаАнотація:
SummaryThe growth of kernels at selected positions along the spike of barley was studied using three models: segmented, logistic and Gompertz. The segmented model divides the growth period into three segments: an initial constant stage, a middle period of ‘linear growth’, and a final constant stage. The identification of a period during which growth is approximately linear - the ‘linear phase’ - was estimated from the curvilinear functions by imposing some criterion to determine the period of ‘linear growth’ in order that the results were similar to those from the segmented model. From this a model of final growth, growth rate and duration of growth as a function of kernel position was developed and fitted to data from four different thinning treatments at five sowing dates. The model of final growth and growth rate was shown to have a family of gamma functions. The analysis of these models showed that there were marked interactions between sowing and thinning treatments for growth rate, less so for grain yield, but there was no substantial interaction for duration.