Multivariate Density Estimation: An SVM Approach

dc.creator	Mukherjee, Sayan
dc.creator	Vapnik, Vladimir
dc.date	2004-10-20T21:04:30Z
dc.date	2004-10-20T21:04:30Z
dc.date	1999-04-01
dc.date.accessioned	2013-10-09T02:48:50Z
dc.date.available	2013-10-09T02:48:50Z
dc.date.issued	2013-10-09
dc.identifier	AIM-1653
dc.identifier	CBCL-170
dc.identifier	http://hdl.handle.net/1721.1/7260
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for multivariate density estimation. The algorithm is based upon a Support Vector Machine (SVM) approach to solving inverse operator problems. The algorithm is implemented and tested on simulated data from different distributions and different dimensionalities, gaussians and laplacians in $R^2$ and $R^{12}$. A comparison in performance is made with Gaussian Mixture Models (GMMs). Our algorithm does as well or better than the GMMs for the simulations tested and has the added advantage of being automated with respect to parameters.
dc.format	7189923 bytes
dc.format	15850137 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-1653
dc.relation	CBCL-170
dc.title	Multivariate Density Estimation: An SVM Approach

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