2006
NeurIPS
NeurIPS 2006
An Oracle Inequality for Clipped Regularized Risk Minimizers
Abstract
We establish a general oracle inequality for clipped approximate minimizers of regularized empirical risks and apply this inequality to support vector machine (SVM) type algorithms. We then show that for SVMs using Gaussian RBF kernels for classification this oracle inequality leads to learning rates that are faster than the ones established in [9]. Finally, we use our oracle inequality to show that a simple parameter selection approach based on a validation set can yield the same fast learning rates without knowing the noise exponents which were required to be known a-priori in [9].
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Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Optimization & Theory > Theory
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Core Methods > Support Vector Machine
Machine Learning > Optimization & Theory > Generalization