Incremental and Decremental SVM for Regression

Training a support vector machine (SVM) for regression (function approximation) in an incremental/decremental way consists essentially in migrating the input vectors in and out of the support vector set with specific modification of the associated thresholds. We introduce with full details such a me...

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Bibliographic Details
Published in:International journal of computers, communications & control communications & control, 2016-12, Vol.11 (6)
Main Authors: Honorius Gí¢lmeanu, Lucian Mircea Sasu, Andonie, Razvan
Format: Article
Language:English
Subjects:
Multilayer perceptrons
Regression
Regularization
Support vector machines
Thresholds
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Summary:Training a support vector machine (SVM) for regression (function approximation) in an incremental/decremental way consists essentially in migrating the input vectors in and out of the support vector set with specific modification of the associated thresholds. We introduce with full details such a method, which allows for defining the exact increments or decrements associated with the thresholds before vector migrations take place. Two delicate issues are especially addressed: the variation of the regularization parameter (for tuning the model performance) and the extreme situations where the support vector set becomes empty. We experimentally compare our method with several regression methods: the multilayer perceptron, two standard SVM implementations, and two models based on adaptive resonance theory.
ISSN:1841-9836
1841-9844
DOI:10.15837/ijccc.2016.6.2744