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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Journal ArticleDOI
Rademacher chaos complexities for learning the kernel problem
Yiming Ying,Colin Campbell +1 more
TL;DR: A novel generalization bound for learning the kernel problem is developed and satisfactory excess generalization bounds and misclassification error rates for learning gaussian kernels and general radial basis kernels are established.
Journal ArticleDOI
Protein subcellular localization prediction using multiple kernel learning based support vector machine
TL;DR: This study aimed to develop an efficient multi-label protein subcellular localization prediction system, named as MKLoc, by introducing multiple kernel learning (MKL) based SVM, and results indicate that MKLoc not only achieves higher accuracy than a single kernel based S VM system but also shows significantly better results than those obtained from other top systems.
Book ChapterDOI
Optimization of the SVM Kernels Using an Empirical Error Minimization Scheme
TL;DR: The present work is an extended experimental study of the framework proposed by Chapelle et al. for optimizing SVM kernels using an analytic upper bound of the error, and minimizes an empirical error estimate using a Quasi-Newton technique.
Proceedings Article
Convex formulations of radius-margin based Support Vector Machines
Huyen Do,Alexandros Kalousis +1 more
TL;DR: Two novel algorithms are presented: R-SVM µ+--a SVM radius-margin based feature selection algorithm, and R- SVM+-- a metric learning-based SVM that achieves a significantly better classification performance compared to SVM and its other state-of-the-art variants.
Journal ArticleDOI
Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm
TL;DR: A new method to select parameters of the Recursive feature elimination algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter.
References
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Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
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