Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Polychotomous kernel Fisher discriminant via top-down induction of binary tree
Computers & Mathematics with Applications
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As a very effective method for universal purpose pattern recognition, support vector machine (SVM) was proposed for dichotomic classification problem, which exhibits a remarkable resistance to overfitting, a feature explained by the fact that it directly implements the principle of structural risk minimization. However, in real world, most of classification problems consist of multiple categories. In an attempt to extend the binary SVM classifier for multiclass classification, decision-tree-based multiclass SVM was proposed recently, in which the structure of decision tree plays an important role in minimizing the classification error. The present study aims at developing a systematic way for the design of decision tree for multiclass SVM. Kernel-induced distance function between datasets was discussed and then kernelized hierarchical clustering was developed and used in determining the structure of decision tree. Further, simulation results on satellite image interpretation show the superiority of the proposed classification strategy over the conventional multiclass SVM algorithms.