Algorithmic approaches to training Support Vector Machines: a survey
Algorithmic approaches to training Support Vector Machines: a survey
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发表时间：
2000
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通讯作者：
C. Campbell
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作者：
C. Campbell
Support V ectorMachines (SVMs) ha vebecome an increasingly popular tool for machine learning tasks involving classi cation, regression or no velty detection. They exhibit good generalisation performance on many reallife datasets and the approach is well-motivated theoretically. T raining involv es optimisation of a convex cost function, there are relatively few free parameters to adjust and the architecture does not have to be found by experimentation. In this tutorial we survey methods for training SVMs including model selection strategies for determining the free parameters and new techniques for active selection of training examples.
