The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
SVM-KM: Speeding SVMs Learning with a priori Cluster Selection and k-Means
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Neighborhood Property--Based Pattern Selection for Support Vector Machines
Neural Computation
Response modeling with support vector regression
Expert Systems with Applications: An International Journal
ϵ-Tube based pattern selection for support vector machines
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Pattern selection for support vector regression based response modeling
Expert Systems with Applications: An International Journal
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Support Vector Machine (SVM) results in a good generalization performance by employing the Structural Risk Minimization (SRM) principle. However, one drawback is O(n3) training time complexity. In this paper, we propose a pattern selection method designed specifically for Support Vector Regression (SVR). In SVR training, only a few patterns called support vectors are used to construct the regression model while other patterns are not used at all. The proposed method tries to select patterns which are likely to become support vectors. With multiple bootstrap samples, we estimate the likelihood of each pattern to become a support vector. The proposed method automatically determines the appropriate number of patterns selected by estimating the expected number of support vectors. Through the experiments involving twenty datasets, the proposed method resulted in the best accuracy among the competing methods.