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FAST toolkit for Hidden Markov Models with Features 1.2.1
by josepablog - April 28, 2015, 17:48:23 CET [
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view (11 today), download ( 3 today ), 1 comment, 1 subscription
- Description:
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 mloss.org.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- Supported Operating Systems: Agnostic
- Data Formats: Tsv
- Tags: Logistic Regression, Expectation Maximization, Maxent, Sequence Modeling
- Archive: download here
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Project details for FAST toolkit for Hidden Markov Models with Features
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Details
- Version:
- 1.2.1
- Submitter:
- josepablog
- Author(s):
- Jose Gonzalez Brenes , Yun Huang
- Posted:
- 28 April 2015
- Last Updated:
- 28 April 2015
- Open Source License:
- gpl
- Programming Language(s):
- Java
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.