Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Pairwise classification and support vector machines
Advances in kernel methods
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
One-class svms for document classification
The Journal of Machine Learning Research
Support Vector Data Description
Machine Learning
Sphere-structured support vector machines for multi-class pattern recognition
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Pattern classification via single spheres
DS'05 Proceedings of the 8th international conference on Discovery Science
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
IEEE Transactions on Fuzzy Systems
A theoretical framework for multi-sphere support vector data description
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
New multi-class classification method based on the SVDD model
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Multiple distribution data description learning algorithm for novelty detection
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Maximum Margin One Class Support Vector Machines for multiclass problems
Pattern Recognition Letters
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Support vector machine (SVM) is a very promising classification technique developed by Vapnik. However, there are still some shortcomings in the original SVM approach. First, SVM was originally designed for binary classification. How to extend it effectively for multiclass classification is still an on-going research issue. Second, SVM does not consider the distribution of each class. In this paper, we propose an extension to the SVM method of pattern recognition for solving the multi-class problem in one formal step. Contrast to previous multi-class SVMs, our approach considers the distribution of each class. Experimental results show that the proposed method is more suitable for practical use than other multi-class SVMs, especially for unbalanced datasets.