Abstract
In this paper, we discuss an improved sparse least support vector training in the reduced empirical feature space which is generated by linearly independent training data. In this method, we select the linearly independent training data as the basis vectors of empirical feature space. Then, before we select these data, we sort training data in ascending order from the standpoint of classification with the values of objective function in training least squares support vector machines. Thus, good training data from the standpoint of classification can be selected in preference as the basis vectors of the empirical feature space. Next, we train least squares support vector machine in the empirical feature space. Then, the solution is sparse since the number of support vectors is equal to that of the basis vectors. Using two-class problems, we evaluate the effectiveness of the proposed method over the conventional methods.
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Kitamura, T., Asano, K. (2015). Sparse LS-SVM in the Sorted Empirical Feature Space for Pattern Classification. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_60
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DOI: https://doi.org/10.1007/978-3-319-26532-2_60
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