The Experts below are selected from a list of 91728 Experts worldwide ranked by ideXlab platform
Peter Sollich - One of the best experts on this subject based on the ideXlab platform.
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Model Selection for support vector machine classification
Neurocomputing, 2003Co-Authors: Carl Gold, Peter SollichAbstract:We address the problem of Model Selection for Support Vector Machine (SVM) classification. For fixed functional form of the kernel, Model Selection amounts to tuning kernel parameters and the slack penalty coefficient C. We begin by reviewing a recently developed probabilistic framework for SVM classification. An extension to the case of SVMs with quadratic slack penalties is given and a simple approximation for the evidence is derived, which can be used as a criterion for Model Selection. We also derive the exact gradients of the evidence in terms of posterior averages and describe how they can be estimated numerically using Hybrid Monte-Carlo techniques. Though computationally demanding, the resulting gradient ascent algorithm is a useful baseline tool for probabilistic SVM Model Selection, since it can locate maxima of the exact (unapproximated) evidence. We then perform extensive experiments on several benchmark data sets. The aim of these experiments is to compare the performance of probabilistic Model Selection criteria with alternatives based on estimates of the test error, namely the so-called “span estimate” and Wahba's Generalized Approximate Cross-Validation (GACV) error. We find that all the “simple” Model criteria (Laplace evidence approximations, and the span and GACV error estimates) exhibit multiple local optima with respect to the hyperparameters. While some of these give performance that is competitive with results from other approaches in the literature, a significant fraction lead to rather higher test errors. The results for the evidence gradient ascent method show that also the exact evidence exhibits local optima, but these give test errors which are much less variable and also consistently lower than for the simpler Model Selection criteria.
Carl Gold - One of the best experts on this subject based on the ideXlab platform.
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Model Selection for support vector machine classification
Neurocomputing, 2003Co-Authors: Carl Gold, Peter SollichAbstract:We address the problem of Model Selection for Support Vector Machine (SVM) classification. For fixed functional form of the kernel, Model Selection amounts to tuning kernel parameters and the slack penalty coefficient C. We begin by reviewing a recently developed probabilistic framework for SVM classification. An extension to the case of SVMs with quadratic slack penalties is given and a simple approximation for the evidence is derived, which can be used as a criterion for Model Selection. We also derive the exact gradients of the evidence in terms of posterior averages and describe how they can be estimated numerically using Hybrid Monte-Carlo techniques. Though computationally demanding, the resulting gradient ascent algorithm is a useful baseline tool for probabilistic SVM Model Selection, since it can locate maxima of the exact (unapproximated) evidence. We then perform extensive experiments on several benchmark data sets. The aim of these experiments is to compare the performance of probabilistic Model Selection criteria with alternatives based on estimates of the test error, namely the so-called “span estimate” and Wahba's Generalized Approximate Cross-Validation (GACV) error. We find that all the “simple” Model criteria (Laplace evidence approximations, and the span and GACV error estimates) exhibit multiple local optima with respect to the hyperparameters. While some of these give performance that is competitive with results from other approaches in the literature, a significant fraction lead to rather higher test errors. The results for the evidence gradient ascent method show that also the exact evidence exhibits local optima, but these give test errors which are much less variable and also consistently lower than for the simpler Model Selection criteria.
Lars S Jermiin - One of the best experts on this subject based on the ideXlab platform.
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Modelfinder fast Model Selection for accurate phylogenetic estimates
Nature Methods, 2017Co-Authors: Subha Kalyaanamoorthy, Bui Quang Minh, Thomas K F Wong, Arndt Von Haeseler, Lars S JermiinAbstract:ModelFinder is a fast Model-Selection method that greatly improves the accuracy of phylogenetic estimates. Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and Model Selection is a key step in all such analyses. We present ModelFinder, a fast Model-Selection method that greatly improves the accuracy of phylogenetic estimates by incorporating a Model of rate heterogeneity across sites not previously considered in this context and by allowing concurrent searches of Model space and tree space.
Paul Joyce - One of the best experts on this subject based on the ideXlab platform.
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Model Selection in phylogenetics
Annual Review of Ecology Evolution and Systematics, 2005Co-Authors: Jack Sullivan, Paul JoyceAbstract:▪ Abstract Investigation into Model Selection has a long history in the statistical literature. As Model-based approaches begin dominating systematic biology, increased attention has focused on how Models should be selected for distance-based, likelihood, and Bayesian phylogenetics. Here, we review issues that render Model-based approaches necessary, briefly review nucleotide-based Models that attempt to capture relevant features of evolutionary processes, and review methods that have been applied to Model Selection in phylogenetics: likelihood-ratio tests, AIC, BIC, and performance-based approaches.
Subha Kalyaanamoorthy - One of the best experts on this subject based on the ideXlab platform.
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Modelfinder fast Model Selection for accurate phylogenetic estimates
Nature Methods, 2017Co-Authors: Subha Kalyaanamoorthy, Bui Quang Minh, Thomas K F Wong, Arndt Von Haeseler, Lars S JermiinAbstract:ModelFinder is a fast Model-Selection method that greatly improves the accuracy of phylogenetic estimates. Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and Model Selection is a key step in all such analyses. We present ModelFinder, a fast Model-Selection method that greatly improves the accuracy of phylogenetic estimates by incorporating a Model of rate heterogeneity across sites not previously considered in this context and by allowing concurrent searches of Model space and tree space.
