Choosing Multiple Parameters for Support Vector Machines
TLDR
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters.Abstract:
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.read more
Citations
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Dealing with high-dimensional class-imbalanced datasets: Embedded feature selection for SVM classification
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Proceedings ArticleDOI
Short-term wind power prediction based on wavelet transform-support vector machine and statistic characteristics analysis
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Fuzziness based sample categorization for classifier performance improvement
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ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions
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Dissertation
Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning
TL;DR: Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian process is used to describe the Bayesian a priori uncertainty about a latent function, and it will be shown how this can be used to estimate value functions.
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