The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Note on the Universal Approximation Capability of Support Vector Machines
Neural Processing Letters
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
An introduction to variable and feature selection
The Journal of Machine Learning Research
Benefitting from the variables that variable selection discards
The Journal of Machine Learning Research
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Brain tumor classification based on long echo proton MRS signals
Artificial Intelligence in Medicine
IEEE Transactions on Neural Networks
Detection of valvular heart disorders using wavelet packet decomposition and support vector machine
Expert Systems with Applications: An International Journal
Kernel based nonlinear dimensionality reduction for microarray gene expression data analysis
Expert Systems with Applications: An International Journal
The Bounds on the Rate of Uniform Convergence of Learning Process on Uncertainty Space
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
The Key Theorem of Learning Theory on Uncertainty Space
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
The theoretical fundamentals of learning theory based on fuzzy complex random samples
Fuzzy Sets and Systems
Computer Methods and Programs in Biomedicine
Combination of feature selection approaches with SVM in credit scoring
Expert Systems with Applications: An International Journal
Importance degree of features and feature selection
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Feature set reduction by evolutionary selection and construction
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Robust RHC method with adaptive DA converter applied to BMI based robotic wheelchair
MACMESE'11 Proceedings of the 13th WSEAS international conference on Mathematical and computational methods in science and engineering
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A support vector machine (SVM) is a novel classifier based on the statistical learning theory. To increase the performance of classification, the approach of SVM with kernel is usually used in classification tasks. In this study, we first attempted to investigate the performance of SVM with kernel. Several kernel functions, polynomial, RBF, summation, and multiplication were employed in the SVM and the feature selection approach developed [Hermes, L., & Buhmann, J. M. (2000). Feature selection for support vector machines. In Proceedings of the international conference on pattern recognition (ICPR'00) (Vol. 2, pp. 716-719)] was utilized to determine the important features. Then, a hypertension diagnosis case was implemented and 13 anthropometrical factors related to hypertension were selected. Implementation results show that the performance of combined kernel approach is better than the single kernel approach. Compared with backpropagation neural network method, SVM based method was found to have a better performance based on two epidemiological indices such as sensitivity and specificity.