2006
NIPS
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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Conference Pioneer
— NIPS 2006
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Keyword Pioneer
— oracle inequalities
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Hot Topic Early Bird
— learning rate
Authors
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 > Learning Types > Classification
Machine Learning > Core Methods > Support Vector Machine