2007
NIPS
NeurIPS 2007
A Risk Minimization Principle for a Class of Parzen Estimators
Abstract
This paper explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms. It is shown that the application of this risk minimization principle results in a class of (computationally) simple learning machines similar to the classical Parzen window classifier. A direct relation with the Rademacher complexities is established, as such facilitating analysis and providing a notion of certainty of prediction. This analysis is related to Support Vector Machines by means of a margin transformation. The power of the MAM principle is illustrated further by application to ordinal regression tasks, resulting in an $O(n)$ algorithm able to process large datasets in reasonable time.
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Keyword Pioneer
— margin classification
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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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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Trend Setter
— Classification
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Core Methods > Support Vector Machine
Deep Learning > Learning Types > Classification