Speeding-up Model Selection for Support Vector Machines

DucDung Nguyen and TuBao Ho, Japan Advanced Institute of Science and Technology

One big difficulty in the practical use of support vector machines is the selection of a suitable kernel function and its appropriate parameter setting for a given application. There is no rule for the selection and people have to estimate the machine’s performance based on a costly multi-trial iteration of training and testing phases. In this paper, we describe a method to reduce the model selection training time for support vector machines. The main idea is, in the process of trying a series of models, the support vectors in previously trained machines are used to initialize the working set in training a new machine. This initialization helps to reduce the number of required optimization loops, thus reducing the training time of the model selection process. Experimental results on real-life datasets show that the training time for each subsequent machine can be reduced effectively in a variety of situations in the model selection process. The method is applicable to different model search strategies and does not affect the model selection result.


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