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https://www.researchgate.net/publication/221080472_The_Bayesian_Committee_Support_Vector_Machine
Another related technique is the Bayesian Committee Support Vector Machine presented in Schwaighofer and Tresp (2001). This technique also partitions the training set into several randomly chosen ...
https://link.springer.com/chapter/10.1007%2F3-540-44668-0_58
Aug 17, 2001 · Empirical evidence indicates that the training time for the support vector machine (SVM) scales to the square of the number of training data points. In this paper, we introduce the Bayesian committee support vector machine (BC-SVM) and achieve an algorithm for training the SVM which scales linearly in the number of training data points.Cited by: 18
http://www.dbs.ifi.lmu.de/%7Etresp/papers/icannsvm_final.pdf
support vector machine (SVM) scales to the square of the number of training data points. In this paper, we introduce the Bayesian committee support vector machine (BC-SVM) and achieve an algorithm for training the SVM which scales linearly in the number of training data points. We verify the good performance of the BC-SVM using several data sets.
https://link.springer.com/chapter/10.1007%2F978-3-319-71249-9_19
Dec 30, 2017 · We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our …Cited by: 4
https://en.wikipedia.org/wiki/Support_vector_machine
Support-vector machine weights have also been used to interpret SVM models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences.
http://www.dbs.ifi.lmu.de/%7Etresp/papers/bcm6.pdf
in form of a Bayesian committee machine (BCM). This scheme is an extension to the input dependent averaging of estimators, a procedure which was introduced by Tresp and Taniguchi, 1995 and Taniguchi and Tresp, 1997. A particular application of our solution is online learning where data arrive sequentially and training must be performed sequen-
http://people.ee.duke.edu/~lcarin/svm_nips2014.pdf
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling Ricardo Henao, Xin Yuan and Lawrence Carin Department of Electrical and Computer Engineering Duke University, Durham, NC 27708 fr.henao,xin.yuan,[email protected] Abstract A new Bayesian formulation is developed for nonlinear support vector machines
https://people.eecs.berkeley.edu/~jordan/papers/zhang-uai06.pdf
Bayesian Multicategory Support Vector Machines Zhihua Zhang Electrical and Computer Engineering University of California Santa Barbara, CA 93106 Michael I. Jordan Computer Science and Statistics University of California Berkeley, CA 94720 Abstract We show that the multi-class support vec-tor machine (MSVM) proposed by Lee et al.
https://dl.acm.org/citation.cfm?id=1121903
The Bayesian committee machine (BCM) is a novel approach to combining estimators that were trained on different data sets. Although the BCM can be applied to the combination of any kind of estimators, the main foci are gaussian process regression and related systems such as regularization networks and smoothing splines for which the degrees of freedom increase with the number of training data.Cited by: 374
https://papers.nips.cc/paper/5507-bayesian-nonlinear-support-vector-machines-and-discriminative-factor-modeling.pdf
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling Ricardo Henao, Xin Yuan and Lawrence Carin Department of Electrical and Computer Engineering Duke University, Durham, NC 27708 {r.henao,xin.yuan,lcarin}@duke.edu Abstract A new Bayesian formulation is developed for nonlinear support vector machinesCited by: 15
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