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https://papers.nips.cc/paper/1722-bayesian-model-selection-for-support-vector-machines-gaussian-processes-and-other-kernel-classifiers.pdf
Bayesian Model Selection for Support Vector Machines 605 absorb A into O. For the SVC loss, (3) can be transformed into a dual problem via y = Ka, where a is a vector of dual variables, which can be efficiently solved using quadratic programming techniques.
https://arxiv.org/pdf/cond-mat/0203334.pdf
Keywords: Support Vector Machines, model selection, probabilistic methods, Bayesian evidence 1 Introduction Support Vector Machines (SVMs) have emerged in recent years as powerful techniques both for regression and classification. One of the central open questions is model selection: how does one tune the parameters of the SVM
https://www.researchgate.net/publication/49459307_Bayesian_Model_Selection_for_Support_Vector_Machines_Gaussian_Processes_and_Other_Kernel_Classifiers
We present a variational Bayesian method for model selection over families of kernels classifiers like Support Vector machines or Gaussian processes.
https://infoscience.epfl.ch/record/161324
We present a variational Bayesian method for model selection over families of kernels classifiers like Support Vector machines or Gaussian processes. The algorithm needs no user interaction and is able to adapt a large number of kernel parameters to given data without having to sacrifice training cases for validation. This opens the possibility to use sophisticated families of kernels in ...Cited by: 152
https://people.eecs.berkeley.edu/~jordan/papers/zhang-uai06.pdf
Bayesian hierarchical model. Sections 4 presents an inferential methodology for this model. Experimen-tal results are presented in Section 5, and concluding remarks are given in Section 6. 2 Probabilistic Multicategory Support Vector Machines Consider a classi cation problem with c classes. We are given a set of training data fxi;yign 1 where ...
https://www.researchgate.net/publication/2461310_Model_Selection_for_Support_Vector_Machines
Kernel methods like e.g. Support vector machines (SVM) and Relevance vector machines (RVM) are widely used as data-mining tools. The concept of Bayesian learning exploited in RVM leads to ...
https://link.springer.com/chapter/10.1007/978-3-540-35488-8_19
Abstract. In this chapter, we develop and evaluate a feature selection algorithm for Bayesian support vector machines. The relevance level of features are represented by ARD (automatic relevance determination) parameters, which are optimized by maximizing the model evidence in the Bayesian …Cited by: 5
https://link.springer.com/article/10.1023%2FA%3A1012489924661
Jan 01, 2002 · I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilistic interpretation can provide intuitive guidelines for choosing a ‘good’ SVM kernel. Beyond this, it allows Bayesian methods to be used for tackling two of the outstanding challenges in SVM classification: …Cited by: 258
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.4315
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We present a variational Bayesian method for model selection over families of kernels classifiers like Support Vector machines or Gaussian processes. The algorithm needs no user interaction and is able to adapt a large number of kernel parameters to given data without having to sacrifice training cases for validation.
https://www.sciencedirect.com/science/article/pii/S0925231203003758
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.Cited by: 206
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