<--- Back to Details
First PageDocument Content
Statistics / Geometry / Statistical distance / Estimation theory / Statistical theory / Bregman divergence / Divergence / Expectationmaximization algorithm / Exponential family / KullbackLeibler divergence / Variational Bayesian methods
Date: 2015-07-31 19:00:25
Statistics
Geometry
Statistical distance
Estimation theory
Statistical theory
Bregman divergence
Divergence
Expectationmaximization algorithm
Exponential family
KullbackLeibler divergence
Variational Bayesian methods

That was fast! Speeding up NN search of high dimensional distributions.

Add to Reading List

Source URL: eceweb.ucsd.edu

Download Document from Source Website

File Size: 420,38 KB

Share Document on Facebook

Similar Documents

That was fast! Speeding up NN search of high dimensional distributions. Emanuele Coviello University of California, San Diego, 9500 Gilman Dr, La Jolla, CA 92093

That was fast! Speeding up NN search of high dimensional distributions. Emanuele Coviello University of California, San Diego, 9500 Gilman Dr, La Jolla, CA 92093

DocID: 1rox3 - View Document

That was fast! Speeding up NN search of high dimensional distributions.

That was fast! Speeding up NN search of high dimensional distributions.

DocID: 1qzf9 - View Document

c 2007 Society for Industrial and Applied Mathematics  SIAM J. MATRIX ANAL. APPL. Vol. 29, No. 4, pp. 1120–1146

c 2007 Society for Industrial and Applied Mathematics  SIAM J. MATRIX ANAL. APPL. Vol. 29, No. 4, pp. 1120–1146

DocID: 1pHxz - View Document

Nonparametric estimation of the likelihood ratio and divergence functionals 1  XuanLong Nguyen1 , Martin J. Wainwright1,2 and Michael I. Jordan1,2

Nonparametric estimation of the likelihood ratio and divergence functionals 1 XuanLong Nguyen1 , Martin J. Wainwright1,2 and Michael I. Jordan1,2

DocID: 1ncML - View Document

Journal of Machine Learning Research1890  Submitted 12/10; Revised 10/11; Published 6/12 Regularization Techniques for Learning with Matrices Sham M. Kakade

Journal of Machine Learning Research1890 Submitted 12/10; Revised 10/11; Published 6/12 Regularization Techniques for Learning with Matrices Sham M. Kakade

DocID: 1mcpE - View Document