Relevant bibliographies by topics / RBF-SVM

Academic literature on the topic 'RBF-SVM'

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Published: 11 September 2023

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Journal articles on the topic "RBF-SVM"

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Indraswari, Rarasmaya, and Agus Zainal Arifin. "RBF KERNEL OPTIMIZATION METHOD WITH PARTICLE SWARM OPTIMIZATION ON SVM USING THE ANALYSIS OF INPUT DATA’S MOVEMENT." Jurnal Ilmu Komputer dan Informasi 10, no. 1 (February 28, 2017): 36. http://dx.doi.org/10.21609/jiki.v10i1.410.

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SVM (Support Vector Machine) with RBF (Radial Basis Function) kernel is a frequently used classification method because usually it provides an accurate results. The focus about most SVM optimization research is the optimization of the the input data, whereas the parameter of the kernel function (RBF), the sigma, which is used in SVM also has the potential to improve the performance of SVM when optimized. In this research, we proposed a new method of RBF kernel optimization with Particle Swarm Optimization (PSO) on SVM using the analysis of input data’s movement. This method performed the optimization of the weight of the input data and RBF kernel’s parameter at once based on the analysis of the movement of the input data which was separated from the process of determining the margin on SVM. The steps of this method were the parameter initialization, optimal particle search, kernel’s parameter computation, and classification with SVM. In the optimal particle’s search, the cost of each particle was computed using RBF function. The value of kernel’s parameter was computed based on the particles’ movement in PSO. Experimental result on Breast Cancer Wisconsin (Original) dataset showed that this RBF kernel optimization method could improve the accuracy of SVM significantly. This method of RBF kernel optimization had a lower complexity compared to another SVM optimization methods that resulted in a faster running time.
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Schuhmann, Ricardo M., Andreas Rausch, and Thomas Schanze. "Parameter estimation of support vector machine with radial basis function kernel using grid search with leave-p-out cross validation for classification of motion patterns of subviral particles." Current Directions in Biomedical Engineering 7, no. 2 (October 1, 2021): 121–24. http://dx.doi.org/10.1515/cdbme-2021-2031.

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Abstract The classification of subviral particle motion in fluorescence microscopy video sequences is relevant to drug development. This work introduces a method for estimating parameters for support vector machines (SVMs) with radial basis function (RBF) kernels using grid search with leave-pout cross-validation for classification of subviral particle motion patterns. RBF-SVM was trained and tested with a large number of combinations of expert-evaluated training and test data sets for different RBF-SVM parameters using grid search. For each subtest, the mean and standard deviation of the accuracy of the RBF-SVM were calculated. The RBF-SVM parameters are selected according to the optimal accuracy. For the optimal parameters, the accuracy is 89% +- 13% for N = 100. Using the introduced computer intensive machine learning parameter adjustment method, an RBF-SVM has been successfully trained to classify the motion patterns of subviral particles into chaotic, moderate and linear movements.
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Harafani, Hani. "Forward Selection pada Support Vector Machine untuk Memprediksi Kanker Payudara." Jurnal Infortech 1, no. 2 (January 14, 2020): 131–39. http://dx.doi.org/10.31294/infortech.v1i2.7398.

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Kanker payudara merupakan masalah kesehatan yang serius, sehingga deteksi dini dari kanker payudara dapat berperan penting dalam perencanaan pengobatan. Pada penelitian ini Support Vector Machine dengan kernel (dot, polynomial, RBF) dan forward selection diterapkan. Perbandingan akurasi SVM tanpa forward selection dengan menggunakan forward selection menunjukkan selisih yang besar. Hasil penelitian menunjukkan SVM(RBF)+FS unggul dengan akurasi 85,38% dibandingkan dengan SVM(Polynomial & dot), selain itu SVM(RBF)+FS juga unggul dibandingkan algoritma machine learning lainnya dalam memprediksi dataset kanker payudara Coimbra.
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Zafari, Azar, Raul Zurita-Milla, and Emma Izquierdo-Verdiguier. "Evaluating the Performance of a Random Forest Kernel for Land Cover Classification." Remote Sensing 11, no. 5 (March 8, 2019): 575. http://dx.doi.org/10.3390/rs11050575.

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The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known and recurrently used methods for this task. In this paper, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectral WorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses. For each study area, SVM-RFK, RF, and SVM-RBF were trained and tested under different conditions over ten subsets. The spectral features for Sukumba were extended by obtaining vegetation indices (VIs) and grey-level co-occurrence matrices (GLCMs), the Salinas dataset is used as benchmarking with its original number of features. In Sukumba, the overall accuracies (OAs) based on the spectral features only are of 81.34 % , 81.08 % and 82.08 % for SVM-RFK, RF, and SVM-RBF. Adding VI and GLCM features results in OAs of 82 % , 80.82 % and 77.96 % . In Salinas, OAs are of 94.42 % , 95.83 % and 94.16 % . These results show that SVM-RFK yields slightly higher OAs than RF in high dimensional and noisy experiments, and it provides competitive results in the rest of the experiments. They also show that SVM-RFK generates highly competitive results when compared to SVM-RBF while substantially reducing the time and computational cost associated with parametrizing the kernel. Moreover, SVM-RFK outperforms SVM-RBF in high dimensional and noisy problems. RF was also used to select the most important features for the extended dataset of Sukumba; the SVM-RFK derived from these features improved the OA of the previous SVM-RFK by 2%. Thus, the proposed SVM-RFK classifier is as at least as good as RF and SVM-RBF and can achieve considerable improvements when applied to high dimensional data and when combined with RF-based feature selection methods.
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Eckstein, Jan, Negin Moghadasi, Hermann Körperich, Elena Weise Valdés, Vanessa Sciacca, Lech Paluszkiewicz, Wolfgang Burchert, and Misagh Piran. "A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function." Diagnostics 12, no. 11 (November 4, 2022): 2693. http://dx.doi.org/10.3390/diagnostics12112693.

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Background: This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Methods: Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years (±7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Results: Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. Conclusion: SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics.
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Kumar, Kapil. "Comprehensive Composition to Spot Intrusions by Optimized Gaussian Kernel SVM." International Journal of Knowledge-Based Organizations 12, no. 1 (January 2022): 1–27. http://dx.doi.org/10.4018/ijkbo.291689.

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The intrusion interjects network devices and holds a switch of the network with the command which regulates the programmer and programmer govern the nasty code inoculated in the device for attaining intelligence about the devices. In this paper, the researchers organized the IDS framework by using machine learning algorithms like Linear SVM, RBF SVM, Sigmoid SVM, and Polynomial SVM to detect intrusions and estimate the performance of numerous algorithms for attaining the optimized algorithm. The researchers utilized the KDDCUP99 for equating the accuracy, precision, and recall of the algorithms, and for classifications, the researchers utilized the binary encoder tools. The performance analysis calculates that RBF SVM is the finest classifier amongst the other SVMs, and the prediction report predicts that Linear SVM results with 99.2% accuracy, Sigmoid SVM results with 99.7% accuracy, Polynomial SVM results with 99.5% accuracy, and RBF SVMs results with 99.99% accuracy.
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Jahed Armaghani, Danial, Panagiotis G. Asteris, Behnam Askarian, Mahdi Hasanipanah, Reza Tarinejad, and Van Van Huynh. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness." Sustainability 12, no. 6 (March 12, 2020): 2229. http://dx.doi.org/10.3390/su12062229.

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The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models. To this end, the authors developed eight SVM models with different kernel types, i.e., the radial basis function (RBF), the linear (LIN), the sigmoid (SIG), and the polynomial (POL). Four of these models were developed using only the SVM method, while the four other models were hybridized with a feature selection (FS) technique. The performance of each model was assessed using five performance indices and a simple ranking system. The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models. Concerning the importance of variables for predicting the brittleness index (BI), the Schmidt hammer rebound number (Rn) was identified as the most important variable by the three single-based models, developed by POL, SIG, and LIN kernels. However, the single SVM model developed by RBF identified density as the most important input variable. Concerning the hybrid SVM models, three models that were developed using the RBF, POL, and SIG kernels identified the point load strength index as the most important input, while the model developed using the LIN identified the Rn as the most important input. All four single-based SVM models identified the p-wave velocity (Vp) as the least important input. Concerning the least important factors for predicting the BI of the rock in hybrid-based models, Vp was identified as the least important factor by FS-SVM-POL, FS-SVM-SIG, and FS-SVM-LIN, while the FS-SVM-RBF identified Rn as the least important input.
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Mohammed, Ahmed Saud, Atheer Saleem Almawla, and Salah Sabbar Thameel. "Prediction of Monthly Evaporation Model Using Artificial Intelligent Techniques in the Western Desert of Iraq-Al-Ghadaf Valley." Mathematical Modelling of Engineering Problems 9, no. 5 (December 13, 2022): 1261–70. http://dx.doi.org/10.18280/mmep.090513.

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The use of traditional methods to predict evaporation may face many obstacles due to the influence of many factors on the pattern of evaporation's shape. Therefore, the use of existing methods of artificial intelligence is a reliable prediction model in many applications in engineering. Monthly measurements were employed in the present work to predict for duration eighteen years, from beginning of January 2000 until December 2017. The best model was chosen using ANNs (MLP, RBF) and AI (SVM) techniques. The best evaporation model prediction was made using ANNs (MLP, RBF) and AI (SVM) technologies, with temperature, wind speed, relative humidity, and sunshine hours used as independent variables. Several statistical metrics have been used to evaluate the effectiveness of the proposed model to other popular artificial intelligence models. The obtained result denotes the superiority of the MLP models over the RBF and SVM models. It is concluded that the MLP model is better than RBF and SVM for evaporation prediction for both groups. A comparison of the model performance between MLP, RBF, and SVM models indicated that the MLP-ANN method presents the best estimates of monthly evaporation rate with minimum RMSE 0.033, minimum MAE 0.026, and maximum determination coefficient 0.967.
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Amelia, Octavia Dwi, Agus M. Soleh, and Septian Rahardiantoro. "Pemodelan Support Vector Machine Data Tidak Seimbang Keberhasilan Studi Mahasiswa Magister IPB." Xplore: Journal of Statistics 2, no. 1 (June 30, 2018): 33–40. http://dx.doi.org/10.29244/xplore.v2i1.76.

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Bogor Agricultural University Postgraduate School (SPs-IPB) can maintain its reputation by applying a more selective admissions system. This research predicts the success of student using Support Vector Machine (SVM) modeling by considering the characteristics and educational background of the students. But there is an imbalance of data class. SVM modeling on unbalanced data produces poor performance with a sensitivity value of 0.00%. Unbalanced data handling using Sythetic Minority Oversampling Technique (SMOTE) succeeded in improving SVM classification performance in classifying unsuccessful students. Based on accuracy, sensitivity, and specificity with the default cut off, the exact type of SVM to model student success is SVM RBF. When using the optimum cut-off value from each type of SVM, the sensitivity value can be improved again. SVM RBF still gives the best result when using cut off 0.6. The final model that will be used to predict the success of the SPs-IPB student is obtained from SVM RBF modeling with cut off 0.6 using the entire data that has been through the SMOTE stage.
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Syahrial, Syahrial, Rosmin Ilham, Zulaika F. Asikin, and St Surya Indah Nurdin. "Stunting Classification in Children's Measurement Data Using Machine Learning Models." Journal La Multiapp 3, no. 2 (March 31, 2022): 52–60. http://dx.doi.org/10.37899/journallamultiapp.v3i2.614.

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The study conducted a stunting classification of measurement data for children under 5 years old. The dataset has attributes such as: gender, age, weight (BB), height (TB), weight / height (BBTB), weight / age (BBU), and height / age (TBU). The research uses the CRISP-DM methodology in processing the data. The data were tested on several classification models, namely: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), classification and regression trees (CART), nave bayes (NB), support vector machine - linear kernel (SVM-Linear), support vector machine - rbf kernel (SVM-RBF), random forest classifier (RPC), adaboost (ADA), and neural network (MLPC). These models were tested on the dataset to find out the best model in accuracy. The test results show that SVM-RBF produces an accuracy of 78%. SVM-RBF has consistently been at the highest accuracy in several tests. Testing through k-fold cross validation with k=10.
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Dissertations / Theses on the topic "RBF-SVM"

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MISHRA, OM. "HUMAN MOTION ANALYSIS." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18772.

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Human motion analysis in the video has its vast application. The recognition of the human action is the most prominent application of human motion analysis. In this research we analyzed different methodologies for modeling human action. We also discussed challenges and methodologies which are used to handle them. These methodologies are divided into two categories. One is global feature descriptor and other is local feature descriptors. The disadvantage of the global feature descriptor is that they can only give the shape information but fails to give motion information. The local feature descriptors are used to find out the motion information of the action video. The disadvantage is that it cannot give the shape or structure information of the action video. The hybrid descriptors are used to solve these problems but these descriptors suffer from high dimensionality features. In this research we proposed new feature descriptors which are capable to deal with these issues in the following manner: 1) We proposed a new local descriptor evaluated from the Finite Element Analysis for human action recognition. This local descriptor represents the distinctive human poses in the form of the stiffness matrix. This stiffness matrix gives the information of motion as well as shape change of the human body while performing an action. Initially, the human body is represented in the silhouette form. Most prominent points of the silhouette are then selected. This silhouette is discretized into several finite small triangle faces (elements) where the prominent points of the boundaries are the vertices of the triangles. The stiffness matrix of each triangle is then calculated. The feature vector representing the action video iii frame is constructed by combining all stiffness matrices of all possible triangles. These feature vectors are given to the Radial Basis Function-Support Vector Machine (RBF-SVM) classifier. The proposed method shows its superiority over other existing state-of-the-art methods on the challenging datasets Weizmann, KTH, Ballet, and IXMAS. 2) Background cluttering, appearance change due to variation in viewpoint and occlusion are the prominent hurdles that can reduce the recognition rate significantly. Methodologies based on Bag-of-visual-words are very popular because they do not require accurate background subtraction. But the main disadvantage with these methods is that they do not retain the geometrical structural information of the clusters that they form. As a result, they show intra-class mismatching. Furthermore, these methods are very sensitive to noise. Addition of noise in the cluster also results in the misclassification of the action. To overcome these problems we proposed a new approach based on modified Bag-of-visual-word. Proposed methodology retains the geometrical structural information of the cluster based on the calculation of contextual distance among the points of the cluster. Normally contextual distance based on Euclidean measure cannot deal with the noise but in the proposed methodology contextual distance is calculated on the basis of a difference between the contributions of cluster points to maintain its geometrical structure. Later directed graphs of all clusters are formed and these directed graphs are described by the Laplacian. Then the feature vectors representing Laplacian are fed to the Radial Basis Function based Support Vector Machine (RBF-SVM) classifier. iv 3) We also proposed a new feature descriptor to detect abnormality in a video captured for surveillance applications in real-time and also overcome the problem of the curse of dimensionality. To extract features related to any change in the video, non linear Gaussian fuzzy lattice functions have been applied on each frame of the video which results in the formation of fuzzy lattices. These fuzzy lattices have been expressed in the form of Schrödinger equation to find the kinetic energy involved corresponding to any change in the video. A number of the fuzzy lattice has been used as a dimension of the feature. It reduces the dimensionality significantly as compared to other state-of-the-art methods. Finally, the kinetic energy parameter is classified into normal and abnormal activities with the help of SVM-based classifier.
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Kohram, Mojtaba. "Experiments with Support Vector Machines and Kernels." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378112059.

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Salazar, Ruiz Enriqueta. "Desarrollo de modelos predictivos de contaminantes ambientales." Doctoral thesis, Universitat Politècnica de València, 2008. http://hdl.handle.net/10251/2504.

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El desarrollo de modelos matemáticos predictivos de distinto tipos de fenómenos son aplicaciones fundamentales y útiles de las técnicas de Minería de Datos. Un buen modelo se convierte en una excelente herramienta científica que requiere de la existencia y disposición de grandes volúmenes de datos, además de habilidad y considerable tiempo aplicado del investigador para integrar los conocimientos más relevantes y característicos del fenómeno en estudio. En el caso concreto de ésta tesis, los modelos de predicción desarrollados se enfocaron en la predicción contaminantes ambientales como el valor medio de Partículas Finas (PM2.5) presentes en el aire respirable con un tiempo de anticipación de 8 horas y del Ozono Troposférico Máximo (O3) con 24 horas de anticipación. Se trabajó con un interesante conjunto de técnicas de predicción partiendo con herramientas de naturaleza paramétrica tan sencillas como Persistencia, Modelación Lineal Multivariante, así como la técnica semi-paramétrica: Regresión Ridge además de herramientas de naturaleza no paramétrica como Redes Neuronales Artificiales (ANN) como Perceptron Multicapa (MLP), Perceptrón Multi Capa Cuadrática (SMLP), Función de Base Radial (RBF) y Redes Elman, así como Máquinas de Vectores Soporte (SVM), siendo las técnicas no paramétricas las que generalizaron mejor los fenómenos modelizados.
Salazar Ruiz, E. (2008). Desarrollo de modelos predictivos de contaminantes ambientales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2504
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Lee, Jen-Hao, and 李仁豪. "Model Selection of the Bounded SVM Formulation Using the RBF Kernel." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/98455433223164841493.

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碩士
國立臺灣大學
資訊工程學研究所
89
The support vector machine (SVM) has become one of the most promising and popular methods in machine learning. Sound theory and careful implementation make SVM efficient enough to solve moderate to large problems, and the performance has been shown to be competitive with existing methods such as neural networks and decision trees. One remaining problem on the practical use of SVM is the model selection. That is, there are several parameters to tune so that better general accuracy can be achieved. This thesis works on the case of an SVM classification formulation with only bounded constraints using the RBF kernel according to the leave-one-out (loo) rate. A simple framework is approached in the first place, in which loo rates are exactly computed through a given model space. Next, some tricks are utilized to avoid unnecessary computation. Some heuristics are also proposed for locating good areas in more efficient ways according to observations on loo rates and time distribution over the model space. The experiments show that the software developed here performs well both in terms of computational time and loo rates. And the heuristics proposed here should be helpful for other SVM model selection software.
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Soares, Rui Emanuel Paixão. "Driver monitoring systems of fatigue based on eye tracking." Master's thesis, 2017. http://hdl.handle.net/1822/54750.

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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores
Nowadays, road deaths as well as the injuries and monetary losses has become a global crisis. One of the main causes of road accidents is related to driver fatigue caused by sleep deprivation or disorders, being present in about 20% of accidents. Therefore, there is a growing interest in developing equipments capable to detect driver’s drowsiness to avoid potential accidents. In order to detect driver’s drowsiness, several private and public entities from around the world have been working on different technologies. Eye tracking technologies are prominent as it allows a non-intrusive monitoring of driver state. This dissertation integrates a project, which is designed to develop new features for the car’s cockpit of future. In this master’s thesis was intended to develop an algorithm, in which through the provided variables of the eye tracking system of the company Smarteye, can detected symptoms of fatigue of the driver early enough in order to avoid accidents. The development of the algorithm involves the study of Machine Learning (ML) methods able to recognize a pattern related to drowsiness. For this purpose it was necessary to create a database so that it was possible to extract relevant information for the training of the implemented ML models. The parameters provided by eye tracker were collected from volunteers who performed tests in a simulator (DSM - Driver Simulator Mockup). During each test, the participant had to respond to the KSS questionnaire to evaluate the level of fatigue. Having the database, it was necessary to apply it in the training of the Machine Learning models for pattern recognition using the algorithms Multilayer Perceptron (MLP), Deep Belief Networks (DBN), Support Vector Machines (SVM), Radial Basis Function (RBF) and compare between them using performance evaluation methods, such as accuracy, specificity, and sensitivity. In this dissertation it was intended to answer the following questions: 1- Has machine learning the potential to measure the driver’s drowsiness using eye tracking technology? 2- What are the performance of the MLP-BP, RBF, DBN and SVM (machine learning) methods on driver’s drowsiness detection? 3- Which facial features from eye tracking are more related with drowsiness? The results in this dissertation shown that the used Machine Learning models have great performance especially the Multilayer Perceptrons classifier where the facial features PERCLOS and Eyelid Opening were fundamental parameters for the estimation of the driver’s state. These results allow to claim that the eye tracking system together with the algorithm developed are a option to detect driver fatigue, and so, the research on this field should continue so that a final product can contribute to the increase road safety and hence the prevention of driving accidents.
Nos dias de hoje, as mortes na estrada, bem como os feridos e as perdas monetárias tornaram-se numa crise global. Uma das principais causas dos acidentes de viação está relacionada com o cansaço do condutor provocado pela privação ou distúrbios de sono, estando presente em cerca de 20% dos acidentes. A fim de detetar sonolência enquanto alguém está a conduzir, várias entidades privadas e públicas de todo o mundo têm trabalhado em diferentes tecnologias. As tecnologias de eye tracking são proeminentes, uma vez que permitem monitorizar o estado do motorista de forma não intrusiva. Nesta tese de mestrado pretendeu-se desenvolver um algoritmo em que, através das variáveis fornecidas pelo sistema eye tracking da empresa Smarteye, se possa detetar sintomas de fadiga do condutor com antecedência a fim de evitar acidentes. O desenvolvimento do algoritmo passa pelo estudo de métodos de Machine Learning (ML) capazes de reconhecer um padrão relacionado com a sonolência. Para esse efeito foi necessário a criação de uma base de dados para que fosse possível extrair informação relevante para o treino dos modelos implementados de ML. Os parâmetros fornecidos pelo eye tracker foram adquiridos a partir de voluntários que realizaram testes dentro de um simulador (DSM - Driver Simulator Mockup). Tendo a base de dados, foi necessário aplicá-la no treino dos modelos de Machine Learning para o reconhecimento de padrões usando os algoritmos Multilayer Perceptron (MLP), Deep Belief Network (DBN), Support Vector Machines (SVM), Radial Basis Function (RBF) e compará-los entre eles, usando métodos reconhecidos de avaliação de performance, como a precisão, especificidade e sensibilidade. Nesta dissertação procurou-se responder às seguintes questões: 1- A área de Machine Learning tem potencial para reconhecer sonolência no condutor usando a tecnologia de eye tracking? 2- Quais são os desempenhos dos modelos MLP-BP, DBN, RBF e SVM na deteção de sonolência no condutor? 3- Quais são os parâmetros faciais provenientes do eye tracking com maior correlação com a sonolência? Os resultados desta dissertação mostraram que os modelos de Machine Learning utilizados têm grande desempenho especialmente o classificador Multilayer Perceptrons onde as características faciais PERCLOS e Eyelid Opening foram parâmetros fundamentais para a estimação do estado do condutor. Estes resultados permitem afirmar que o sistema de eye tracking juntamente com o algoritmo desenvolvido são uma opção para detetar fadiga do condutor, e portanto a investigação nesta área deve continuar de modo a que se chegue a um produto final que possa contribuir para o aumento da segurança rodoviária.
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Book chapters on the topic "RBF-SVM"

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Jiang, Huiyan, Xiangying Liu, Lingbo Zhou, Hiroshi Fujita, and Xiangrong Zhou. "Morlet-RBF SVM Model for Medical Images Classification." In Advances in Neural Networks – ISNN 2011, 121–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21090-7_14.

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Koul, Nimrita, and Sunilkumar S. Manvi. "Cancer Classification Using Mutual Information and Regularized RBF-SVM." In Machine Learning Technologies and Applications, 327–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4046-6_32.

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El Boujnouni, Mohamed. "Generating Artworks Using One Class SVM with RBF Kernel." In International Conference on Advanced Intelligent Systems for Sustainable Development, 308–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26384-2_27.

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Lu, Zhihai, and Siyuan Lu. "Petal-Image Based Flower Classification via GLCM and RBF-SVM." In Communications in Computer and Information Science, 216–27. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1925-3_16.

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Debnath, Rameswar, and Haruhisa Takahashi. "Learning Capability: Classical RBF Network vs. SVM with Gaussian Kernel." In Developments in Applied Artificial Intelligence, 293–302. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48035-8_29.

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Gowda, Shreyank N. "Fiducial Points Detection of a Face Using RBF-SVM and Adaboost Classification." In Computer Vision – ACCV 2016 Workshops, 590–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54407-6_40.

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Albatal, Rami, and Suzanne Little. "Empirical Exploration of Extreme SVM-RBF Parameter Values for Visual Object Classification." In MultiMedia Modeling, 299–306. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04117-9_28.

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Alabi, Kayode Omotosho, Sulaiman Olaniyi Abdulsalam, Roseline Oluwaseun Ogundokun, and Micheal Olaolu Arowolo. "Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF." In Communications in Computer and Information Science, 158–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69143-1_13.

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Xue, Zihan, Jing Cao, Peizhen Wang, Zihuan Yin, and Dailin Zhang. "An LDA and RBF-SVM Based Classification Method for Inertinite Macerals of Coal." In Lecture Notes in Computer Science, 155–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87358-5_13.

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Liu, Yingpei, Xiangyu Wang, Feifei Zhang, Jianxiang Gao, Yujing Su, Xuewei Zhang, and Haiping Liang. "Fault Classification of Outage Transmission Lines Based on RBF-SVM and BP Neural Networks." In Lecture Notes in Electrical Engineering, 799–805. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8052-6_101.

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Conference papers on the topic "RBF-SVM"

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Emara, Wael, and Mehmed Kantardzic. "The locality of RBF-SVM for incremental learning." In 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2009. http://dx.doi.org/10.1109/cidm.2009.4938671.

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Liu, Yin, and Keshab K. Parhi. "Computing RBF Kernel for SVM Classification Using Stochastic Logic." In 2016 IEEE International Workshop on Signal Processing Systems (SiPS). IEEE, 2016. http://dx.doi.org/10.1109/sips.2016.64.

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Liang, Ruiyu, Yanqiong Ding, Xuewu Zhang, and Jiasheng Chen. "Copper Strip Surface Defects Inspection Based on SVM-RBF." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.271.

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Qu, Li-ping, Hao-han Zhou, Chong-jie Liu, and Zhao Lu. "Study on Multi-RBF-SVM for Transformer Fault Diagnosis." In 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2018. http://dx.doi.org/10.1109/dcabes.2018.00056.

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Liu, Zixuan, Ziyuan Dang, and Jie Yu. "Stock Price Prediction Model Based on RBF-SVM Algorithm." In 2020 International Conference on Computer Engineering and Intelligent Control (ICCEIC). IEEE, 2020. http://dx.doi.org/10.1109/icceic51584.2020.00032.

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Patro, B. Shivalal, Pruthiraj Swain, and B. Vandana. "Macromodel development for Wind Speed Estimation Using RBF-SVM." In 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2021. http://dx.doi.org/10.1109/gucon50781.2021.9573536.

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Ping, Yuan, Mao Zhizhong, and Wang Fuli. "On-line adaptation algorithm for RBF kernel based FS-SVM." In 2011 23rd Chinese Control and Decision Conference (CCDC). IEEE, 2011. http://dx.doi.org/10.1109/ccdc.2011.5968914.

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Emara, Wael, and Mehmed Kantardzic. "Local properties of RBF-SVM during training for incremental learning." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178644.

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Singh, Namrta, Swpanil Agrawal, Tanya Agarwal, and Pavan Kumar Mishra. "RBF-SVM Based Resource Allocation Scheme for 5G CRAN Networks." In 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE). IEEE, 2018. http://dx.doi.org/10.1109/icraie.2018.8710423.

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Tbarki, Khaoula, Salma Ben Said, Riadh Ksantini, and Zied Lachiri. "RBF kernel based SVM classification for landmine detection and discrimination." In 2016 International Image Processing, Applications and Systems (IPAS). IEEE, 2016. http://dx.doi.org/10.1109/ipas.2016.7880146.

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