Searching for Probabilistic Interpretation And Bayesian Methods For Support Vector Machines information? Find all needed info by using official links provided below.
https://www.researchgate.net/publication/2584206_Probabilistic_interpretations_and_Bayesian_methods_for_Support_Vector_Machines
Probabilistic interpretations and Bayesian methods for Support Vector Machines ... This interpretation enables Bayesian methods to be employed to determine the regularisation parameters in the SVM ...Author: Peter Sollich
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
https://nms.kcl.ac.uk/peter.sollich/papers_pdf/SVM_MLMM_Kluwer.pdf
Abstract. 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 forCited by: 258
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 ...
http://web.cs.iastate.edu/~honavar/bayes-svm.pdf
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 …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.7045
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Support Vector Machines (SVMs) can be interpreted as maximum a posteriori solutions to inference problems with Gaussian Process (GP) priors and appropriate likelihood functions. Focussing on the case of classification, I show first that such an interpretation gives a clear intuitive meaning to SVM kernels, as ...
http://www.kernel-machines.org/publications/Sollich99
P. Sollich (1999) . Probabilistic interpretation and Bayesian methods for Support Vector Machines. In: Proceedings of ICANN'99, pp. 91-96, IEE Publications.
http://www.kernel-machines.org/publications/Sollich99/bibliography_exportForm
You are here: Home → Publications → Probabilistic interpretation and Bayesian methods for Support Vector Machines. Navigation. Home ... Publications. Probabilistic interpretation and Bayesian methods for Support Vector Machines. Books. Software. Annual Workshop. JMLR. Links. Tutorials.
https://dl.acm.org/citation.cfm?id=599659
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class ProbabilitiesCited by: 258
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