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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New support vector algorithms with parametric insensitive/margin model
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A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue
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Composite kernel learning
TL;DR: This work proposes Composite Kernel Learning to address the situation where distinct components give rise to a group structure among kernels, and describes the convexity of the learning problem, and provides a general wrapper algorithm for computing solutions.
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Leave-One-Out Bounds for Support Vector Regression Model Selection
Ming-Wei Chang,Chih-Jen Lin +1 more
TL;DR: Experiments demonstrate that the proposed bounds are competitive with Bayesian SVR for parameter selection and the differentiability of leave-one-out bounds is discussed.
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Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines.
TL;DR: In this article, the authors investigated the potential application of GIS-based Support Vector Machines (SVM) with four kernel functions, i.e., radial basis function (RBF), polynomial (PL), sigmoid (SIG), and linear (LN), for landslide susceptibility mapping at Luxi city in Jiangxi province, China.
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