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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7202| Title: | Learning from Incomplete Data |
| Keywords: | AI MIT Artificial Intelligence missing data mixture models statistical learning EM algorithm maximum likelihood neural networks |
| Issue Date: | 9-Oct-2013 |
| 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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-1509 CBCL-108 http://hdl.handle.net/1721.1/7202 |
| Appears in Collections: | MIT Items |
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