Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
The pre-image problem in kernel methods
IEEE Transactions on Neural Networks
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Reduced set method is an important approach to speed up classification process of support vector machine (SVM) by compressing the number of support vectors included in the machine's solution. Existing works find the reduced set vectors based on solving an unconstrained optimization problem with multivariables, which may suffer from numerical instability or get trapped in a local minimum. In this paper, a novel reduced set method relying on kernel-based clustering is presented to simplify SVM solution. This approach is conceptually simpler, involves only linear algebra and overcomes the difficulties existing in former reduced set methods. Experiments on real data sets indicate that the proposed method is effective in simplifying SVM solution while preserving machine's generalization performance.