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Graphical Models for Heterogeneous T...

University of Rochester.

Graphical Models for Heterogeneous Transfer Learning and Co-reference Resolution.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Graphical Models for Heterogeneous Transfer Learning and Co-reference Resolution.
Author:	Wei, Bin.
Description:	100 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 2217.
Contained By:	Dissertation Abstracts International72-04B.
Subject:	Computer Science.
ISBN:	9781124481951
[NT 15000229]:	Traditional supervised machine learning requires labeled data for a specific problem of interest. There have been many attempts to reduce this requirement such as approaches based on semi-supervised learning. In recent years, people have started to consider a new strategy known as transfer learning, where labeled data from an old problem (called the source task) is used to assist the learning of a new but related problem (the target task).
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3442785

Graphical Models for Heterogeneous Transfer Learning and Co-reference Resolution.
Wei, Bin.

Graphical Models for Heterogeneous Transfer Learning and Co-reference Resolution. - 100 p.

Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 2217.

Thesis (Ph.D.)--University of Rochester, 2011.

Traditional supervised machine learning requires labeled data for a specific problem of interest. There have been many attempts to reduce this requirement such as approaches based on semi-supervised learning. In recent years, people have started to consider a new strategy known as transfer learning, where labeled data from an old problem (called the source task) is used to assist the learning of a new but related problem (the target task).

ISBN: 9781124481951Subjects--Topical Terms:

423143
Computer Science.

Graphical Models for Heterogeneous Transfer Learning and Co-reference Resolution.
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020 $a 9781124481951
035 $a (UMI)AAI3442785
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040 $a UMI $c UMI
100 1 $a Wei, Bin. $3 475301
245 1 0 $a Graphical Models for Heterogeneous Transfer Learning and Co-reference Resolution.
300 $a 100 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 2217.
500 $a Adviser: Chris Pal.
502 $a Thesis (Ph.D.)--University of Rochester, 2011.
520 $a Traditional supervised machine learning requires labeled data for a specific problem of interest. There have been many attempts to reduce this requirement such as approaches based on semi-supervised learning. In recent years, people have started to consider a new strategy known as transfer learning, where labeled data from an old problem (called the source task) is used to assist the learning of a new but related problem (the target task).
520 $a In this thesis, we mainly consider an extreme case of transfer learning that we denote as heterogeneous transfer learning - where the feature spaces of the source task and the target tasks are disjoint. We first consider the cross-lingual text classification task, where we need to train a classifier for Chinese but we only have labeled data in English. We adapt the structural correspondence learning (SCL) algorithm for the problem. Furthermore, we generalize the SCL algorithm as a multi-task transfer learning strategy and propose the use of a restricted Boltzmann machine (RBM), a special type of probabilistic graphical models, as an implementation. We also give some preliminary theoretical analysis for the strategy by combining previous work on general transfer learning and multi-task learning.
520 $a Finally, we study the problem of co-reference resolution using another kind of graphical models, the conditional random field (CRF). We show that a previously proposed ranking approach, which produces state of the art results, can be viewed as a special case of the model. We go on to show how using a CRF allows us to easily incorporate other NLP tasks such as non-anaphoric identification and noun phrase boundary detection.
590 $a School code: 0188.
650 4 $a Computer Science. $3 423143
690 $a 0984
710 2 $a University of Rochester. $3 475298
773 0 $t Dissertation Abstracts International $g 72-04B.
790 1 0 $a Pal, Chris, $e advisor
790 $a 0188
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3442785

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