Pairwise classification and support vector machines
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Data Description
Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Half-Against-Half multi-class support vector machines
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
Quantitative Comparison of Similarity Measure and Entropy for Fuzzy Sets
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor
Expert Systems with Applications: An International Journal
Density weighted support vector data description
Expert Systems with Applications: An International Journal
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We propose a novel classification method that can reduce the computational cost of training and testing for multiclass problems. The proposed method uses the distance in feature space between a test sample and high-density region or domain that can be described by support vector learning. The proposed method shows faster training speed and has ability to represent the nonlinearity of data structure using a smaller percentage of available data sample than the existing methods for multiclass problems. To demonstrate the potential usefulness of the proposed approach, we evaluate the performance about artificial and actual data. Experimental results show that the proposed method has better accuracy and efficiency than the existing methods.