Quadratic optimization fine tuning for the learning phase of SVM
ISSADS'05 Proceedings of the 5th international conference on Advanced Distributed Systems
Support Vector Optimization through Hybrids: Heuristics and Math Approach
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Quadratic optimization fine tuning for the Support Vector Machines learning phase
Expert Systems with Applications: An International Journal
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This paper presents a comparison of different initialization algorithms joint with decomposition methods, in order to reduce the training time of Support Vector Machines (SVMs). Training a SVM involves the solution of a quadratic optimization problem (QP).The QP problem is very resource consuming (computational time and computational memory), because the quadratic form is dense and the memory requirements grow square the number of data points. The SVM-QP problem can be solved by several optimization strategies but, for large scale applications, they must be combined with decomposition algorithms that breaks up the entire SVM-QP problem into a series of smaller ones. The support vectors found in the training of SVMs represent a small subgroup of the training patterns. Some algorithms are used to initilizate the SVMs, making a fast approximation of the points standing for support vectors, to train the SVM only with those data. Combination of these initializations algorithms and the decomposition approach, coupled with an QP solver specially arranged for the SVM-QP problem, are compared using some well-known benchmarks in order to show their capabilities.