Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Classification of bio-data with small data set using additive factor model and SVM
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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When we apply support vector machines (SVM) to multi-class classification, some methods of combining the results of independent SVM for each class haven been used, However, the conventional methods may deteriorates generalization performance when the number of data in each class is small. To solve this problem, we proposed a new method, which uses only one SVM and train it to find some similarity measure between data samples. Through an experiment using real data, we confirm that the proposed method can give better classification performance than the conventional one.