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Learning parameters in canonical models using weighted least squares

Nowak, K and Druzdzel, MJ (2014) Learning parameters in canonical models using weighted least squares. In: UNSPECIFIED UNSPECIFIED, 366 - 381. ISBN UNSPECIFIED

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Abstract

© 2014 Springer International Publishing Switzerland. We propose a novel approach to learning parameters of canonical models from small data sets using a concept employed in regression analysis: weighted least squares method. We assess the performance of our method experimentally and show that it typically outperforms simple methods used in the literature in terms of accuracy of the learned conditional probability distributions.


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Details

Item Type: Book Section
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Nowak, K
Druzdzel, MJdruzdzel@pitt.eduDRUZDZEL
Date: 1 January 2014
Date Type: Publication
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 8754
Page Range: 366 - 381
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 0302-9743
Date Deposited: 02 Jul 2015 14:34
Last Modified: 01 Nov 2017 12:58
URI: http://d-scholarship.pitt.edu/id/eprint/25524

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