Project details for FAST toolkit for Hidden Markov Models with Features

Logo FAST toolkit for Hidden Markov Models with Features 1.2.1

by josepablog - April 28, 2015, 17:48:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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).

Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Tsv
Tags: Logistic Regression, Expectation Maximization, Maxent, Sequence Modeling
Archive: download here


Jose Gonzalez-Brenes (on April 29, 2015, 04:25:23)
We have a big update of FAST coming soon in terms of documentation and generalizability. The current implementation is optimized for student model. We are uploading this preliminary version for now to be eligible to participate in the MLOSS workshop at ICML.

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