Optimizing working sets for training support vector regressors by Newton's method

In this paper, we train support vector regressors (SVRs) fusing sequential minimal optimization (SMO) and Newton's method. We use the SVR formulation that includes the absolute variables. A partial derivative of the absolute variable with respect to the associated variable is indefinite when th...

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Bibliographic Details
Published in2015 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
Main Author Abe, Shigeo
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.07.2015
Subjects
Conferences
Derivatives
Integrated circuits
Mathematical analysis
Neural networks
Newton methods
Optimization
Training
Vectors (mathematics)
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Summary:In this paper, we train support vector regressors (SVRs) fusing sequential minimal optimization (SMO) and Newton's method. We use the SVR formulation that includes the absolute variables. A partial derivative of the absolute variable with respect to the associated variable is indefinite when the variable takes on zero. We determine the derivative value according to whether the optimal solution exits in the positive region, negative region, or at zero. In selecting working set, we use the method that we have developed for the SVM, namely, in addition to the pair of variables selected by SMO, loop variables that repeatedly appear in training, are added to the working set. By this method the working set size is automatically determined. We demonstrate the validity of our method over SMO using several benchmark data sets.
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ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2015.7280309