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January 24, 2010 | History

Structure learning in sequential data 1 edition

Structure learning in sequential data
Liam Stewart

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Structure learning in sequential data.

Published 2005 .
Written in English.

About the Book

The goal of discriminative sequence learning is to learn how to classify items that can be arranged in a sequence. Many models have been proposed including logistic regression, the maximum entropy Markov model, the conditional random field, the input output Markov model, the hidden random field, and template models based on restricted Boltzmann machines. These models differ along several dimensions: whether they can be represented by a directed graphical model or an undirected one, whether or not they are chain structured, whether or not they are fully observed models, and whether or not they can incorporate knowledge about larger scale label structures. In this work, we compare these models on several synthetic problems and on a larger information extraction task.

Edition Notes

Source: Masters Abstracts International, Volume: 44-02, page: 0946.

Advisor: Richard Zemel.

Thesis (M.Sc.)--University of Toronto, 2005.

Electronic version licensed for access by U. of T. users.

GERSTEIN MICROTEXT copy on microfiche (2 microfiches).

The Physical Object

Pagination
95 leaves.
Number of pages
95

ID Numbers

Open Library
OL19216593M
ISBN 10
0494071931

History Created December 11, 2009 · 2 revisions Download catalog record: RDF / JSON

January 24, 2010 Edited by WorkBot add more information to works
December 11, 2009 Created by WorkBot add works page