An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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S. Keerthi, Vikas Sindhwani, Olivier Chapelle


We consider the task of tuning hyperparameters in SVM models based on minimizing a smooth performance validation function, e.g., smoothed k-fold crossvalidation error, using non-linear optimization techniques. The key computation in this approach is that of the gradient of the validation function with respect to hyperparameters. We show that for large-scale problems involving a wide choice of kernel-based models and validation functions, this computation can be very efficiently done; often within just a fraction of the training time. Empirical results show that a near-optimal set of hyperparameters can be identified by our approach with very few training rounds and gradient computations. .