أعرض تسجيلة المادة بشكل مبسط

dc.creator Ghahramani, Zoubin
dc.creator Jordan, Michael I.
dc.date 2004-10-20T20:49:37Z
dc.date 2004-10-20T20:49:37Z
dc.date 1995-01-24
dc.date.accessioned 2013-10-09T02:48:32Z
dc.date.available 2013-10-09T02:48:32Z
dc.date.issued 2013-10-09
dc.identifier AIM-1509
dc.identifier CBCL-108
dc.identifier http://hdl.handle.net/1721.1/7202
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and function approximation from incomplete data in a principled and efficient manner. These algorithms are based on mixture modeling and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster, Laird, and Rubin 1977)---both for the estimation of mixture components and for coping with the missing data.
dc.format 11 p.
dc.format 388268 bytes
dc.format 515095 bytes
dc.format application/postscript
dc.format application/pdf
dc.language en_US
dc.relation AIM-1509
dc.relation CBCL-108
dc.subject AI
dc.subject MIT
dc.subject Artificial Intelligence
dc.subject missing data
dc.subject mixture models
dc.subject statistical learning
dc.subject EM algorithm
dc.subject maximum likelihood
dc.subject neural networks
dc.title Learning from Incomplete Data


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أعرض تسجيلة المادة بشكل مبسط