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Constructing Support Vector Machines with Reduced Classifier Complexity
| National Conference on Emerging Trends in Advanced Communication Technologies |
| Foundation of Computer Science USA |
| NCETACT2015 - Number 2 |
| June 2015 |
| Authors: Ankur M. Bobade, N. N. Khalsa, S. M. Deshmukh |
| ac4954e8-34f9-48d7-a0b5-9f85ed52405d |
Ankur M. Bobade, N. N. Khalsa, S. M. Deshmukh . Constructing Support Vector Machines with Reduced Classifier Complexity. National Conference on Emerging Trends in Advanced Communication Technologies. NCETACT2015, 2 (June 2015), 1-5.
Abstract
Support vector machines (SVMs), though perfect, are not chosen in applications requiring great classi?cation speed, due to the number of support vectors being large. To conquer this problem we devise a primitive method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it materialistically ?nds a set of kernel basis functions of a speci?ed maximum size (dmax) to approximate the SVM primitive cost function well; (3) it is ef?cient and roughly scales as O(ndmax2)where n is the number of training examples; and, (4) the number of basis functions it requires to accomplish an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.
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