The FAST toolkit for Unsupervised Learning of HMMs with Features

Huang, Yun and Gonzalez-Brenes, Jose and Brusilovsky, Peter (2015) The FAST toolkit for Unsupervised Learning of HMMs with Features. In: The Machine Learning Open Source Software workshop at the 32nd International Conference on Machine Learning.

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Abstract

FAST, is an toolkit for adding features to Hidden Markov Models (HMM). It implements a recent variation of the Expectation-Maximization algorithm (Berg-Kirkpatrick et al, 2010) that allows to use logistic regression in unsupervised learning. We demonstrate FAST for predicting future student performance. Our toolkit is up to 300x faster than BNT (a Bayesian Network toolkit), and up to 25% better than conventional HMMs (with no features).

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Details

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Creators/Authors:	Creators Email Pitt Username ORCID Huang, Yun yuh43@pitt.edu YUH43 Gonzalez-Brenes, Jose Brusilovsky, Peter peterb@pitt.edu PETERB 0000-0002-1902-1464
Date:	10 July 2015
Date Type:	Publication
Access Restriction:	No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title:	The Machine Learning Open Source Software workshop at the 32nd International Conference on Machine Learning
Event Title:	The Machine Learning Open Source Software workshop at the 32nd International Conference on Machine Learning
Event Type:	Conference
Institution:	University of Pittsburgh
Schools and Programs:	Dietrich School of Arts and Sciences > Intelligent Systems
Refereed:	Yes
Official URL:	http://mloss.org/workshop/icml15/
Date Deposited:	24 Aug 2015 16:35
Last Modified:	01 Nov 2017 12:57
URI:	http://d-scholarship.pitt.edu/id/eprint/26043

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• The FAST toolkit for Unsupervised Learning of HMMs with Features. (deposited 24 Aug 2015 16:35) [Currently Displayed]

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