The nature of statistical learning theory
The nature of statistical learning theory
Pairwise classification and support vector machines
Advances in kernel methods
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
A decision based one-against-one method for multi-class support vector machine
Pattern Analysis & Applications
Nesting Algorithm for Multi-Classification Problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Reducing the number of sub-classifiers for pairwise multi-category support vector machines
Pattern Recognition Letters
Online prediction model based on support vector machine
Neurocomputing
An Improved Hierarchical Multi-class Support Vector Machine with Binary Tree Architecture
ICICSE '08 Proceedings of the 2008 International Conference on Internet Computing in Science and Engineering
Tree-Structured Support Vector Machines for Multi-class Classification
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Binary tree support vector machine based on kernel fisher discriminant for multi-classification
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification
IEEE Transactions on Neural Networks
A vector-valued support vector machine model for multiclass problem
Information Sciences: an International Journal
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Unclassifiable region (UR) in conventional multi-classification support vector machine (MSVM) decreased the classification capacity and generalization ability of MSVM. To overcome the disadvantage, vector projection method (VPM) was presented. VPM first projects the samples in UR onto the line linking every two class centers, then computes the feature distance between each projecting point and corresponding class center. For one sample, the class with smaller feature distance will be voted one time and the sample belongs to the class which owns the most votes. Experimental results on synthetic and benchmark datasets show that VPM resolved the UR problem effectively and improved the classification capacity and generalization ability of MSVM.