Gotowe bibliografie tematyczne / RBF-SVM

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Części książek
  4. Streszczenia konferencji

Gotowa bibliografia na temat „RBF-SVM”

Autor: Grafiati
Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "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 (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 optim
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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 (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 accu
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Harafani, Hani. "Forward Selection pada Support Vector Machine untuk Memprediksi Kanker Payudara." Jurnal Infortech 1, no. 2 (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 mempredik
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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 (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 il
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Eckstein, Jan, Negin Moghadasi, Hermann Körperich, et al. "A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function." Diagnostics 12, no. 11 (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-near
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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 (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 algorithm
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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 (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 assesse
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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 (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) technologi
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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 (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
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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 (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 (S
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Rozprawy doktorskie na temat "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 desc
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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
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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<br>國立臺灣大學<br>資訊工程學研究所<br>89<br>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
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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<br>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
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Części książek na temat "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. 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. 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. 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. 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. 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. 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. 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. 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. 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, et al. "Fault Classification of Outage Transmission Lines Based on RBF-SVM and BP Neural Networks." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8052-6_101.

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Streszczenia konferencji na temat "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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