Active set training of support vector regressors
نویسنده
چکیده
In our previous work we have discussed the training method of a support vector classifier by active set training allowing the solution to be infeasible during training. In this paper, we extend this method to training a support vector regressor (SVR). We use the dual form of the SVR where variables take real values and in the objective function the weighted linear sum of absolute values of the variables is included. We allow the variables to change signs from one step to the next. This means changes of the active inequality constraints. Namely, we solve the quadratic programming problem for the initial working set of training data by Newton’s method, delete from the working set the data within the epsilon tube, add to the working set training data outside of the epsilon tube, and repeat training the SVM until the working set does not change. We demonstrate the effectiveness of the proposed method using some benchmark data sets.
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