The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A generalized S-K algorithm for learning v-SVM classifiers
Pattern Recognition Letters
Neural Computation
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A general soft method for learning SVM classifiers with L1-norm penalty
Pattern Recognition
On the Equivalence of the SMO and MDM Algorithms for SVM Training
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Associated evolution of a support vector machine-based classifier for pedestrian detection
Information Sciences: an International Journal
A novel geometric approach to binary classification based on scaled convex hulls
IEEE Transactions on Neural Networks
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
A common framework for the convergence of the GSK, MDM and SMO algorithms
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
The robust and efficient adaptive normal direction support vector regression
Expert Systems with Applications: An International Journal
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
A Geometric Nearest Point Algorithm for the Efficient Solution of the SVM Classification Task
IEEE Transactions on Neural Networks
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The parametric-margin @n-support vector machine (par-@n-SVM) is a useful classifier in many cases, especially when the noise is heteroscedastic. In this paper, the geometric interpretation for the par-@n-SVM is described, which is equivalent to finding a couple of points in two disjoint @m-reduced convex hulls (@m-RCHs) by simultaneously minimizing the square distance and maximizing the square norm of their sum with a weight factor 1/(c@n) given by users. Motivated by the Gilbert-Schlesinger-Kozinec (GSK) and Mitchell-Dem'yanov-Malozemov (MDM) algorithms, two geometric algorithms, called the parametric @m-GSK(par-@m-GSK) and parametric @m-MDM(par-@m-MDM) algorithms, are introduced to solve the par-@n-SVM. Computational results on several synthetic as well as benchmark datasets demonstrate the significant performance of the proposed algorithms in terms of both kernel operations and classification accuracy.