Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
SIAM Journal on Optimization
Feature Extraction Based on Decision Boundaries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Projected Clustering by Subspace Mining
IEEE Transactions on Knowledge and Data Engineering
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Maximum Relative Margin and Data-Dependent Regularization
The Journal of Machine Learning Research
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Robust twin support vector machine for pattern classification
Pattern Recognition
Computers and Electronics in Agriculture
A sparse support vector machine classifier with nonparametric discriminants
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Structural twin support vector machine for classification
Knowledge-Based Systems
A new approach for manufacturing forecast problems with insufficient data: the case of TFT---LCDs
Journal of Intelligent Manufacturing
Using robust dispersion estimation in support vector machines
Pattern Recognition
An efficient 3D face recognition approach using local geometrical signatures
Pattern Recognition
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Support vector machine (SVM) is a powerful classification methodology, where the support vectors fully describe the decision surface by incorporating local information. On the other hand, nonparametric discriminant analysis (NDA) is an improvement over LDA where the normality assumption is relaxed. NDA also detects the dominant normal directions to the decision plane. This paper introduces a novel SVM+NDA model which can be viewed as an extension to the SVM by incorporating some partially global information, especially, discriminatory information in the normal direction to the decision boundary. This can also be considered as an extension to the NDA where the support vectors improve the choice of k-nearest neighbors on the decision boundary by incorporating local information. Being an extension to both SVM and NDA, it can deal with heteroscedastic and non-normal data. It also avoids the small sample size problem. Moreover, it can be reduced to the classical SVM model, so that existing softwares can be used. A kernel extension of the model, called KSVM+KNDA is also proposed to deal with nonlinear problems. We have carried an extensive comparison of the SVM+NDA to the LDA, SVM, heteroscedastic LDA (HLDA), NDA and the combined SVM and LDA on artificial, real and face recognition data sets. Results for KSVM+KNDA have also been presented. These comparisons demonstrate the advantages and superiority of our proposed model.