2006
NIPS
NeurIPS 2006
Gaussian and Wishart Hyperkernels
Abstract
We propose a new method for constructing hyperkenels and define two promising special cases that can be computed in closed form. These we call the Gaussian and Wishart hyperkernels. The former is especially attractive in that it has an interpretable regularization scheme reminiscent of that of the Gaussian RBF kernel. We discuss how kernel learning can be used not just for improving the performance of classification and regression methods, but also as a stand-alone algorithm for dimensionality reduction and relational or metric learning.
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Conference Pioneer
— NIPS 2006
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Trend Setter
— Probability
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Keyword Pioneer
— wishart distribution
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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, Reinforcement Learning, Robotics, Security & Privacy
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Topic Pioneer
— Kernel Methods
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Hot Topic Early Bird
— dimensionality reduction
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Core Methods > Metric Learning
Mathematics & Optimization > Mathematics > Probability
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Bayesian & Probabilistic > Kernel Methods
Mathematics & Optimization > Optimization > Kernel Methods
Machine Learning > Optimization & Theory > Kernel Methods