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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7202Full metadata record
| DC Field | Value | Language |
|---|---|---|
| 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 | - |
| Appears in Collections: | MIT Items | |
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