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» Incremental Construction of Structured Hidden Markov Models
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NIPS
2008
14 years 5 days ago
Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction
We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilistic context-free gr...
Shay B. Cohen, Kevin Gimpel, Noah A. Smith
EDM
2008
169views Data Mining» more  EDM 2008»
14 years 5 days ago
Mining Student Behavior Models in Learning-by-Teaching Environments
This paper discusses our approach to building models and analyzing student behaviors in different versions of our learning by teaching environment where students learn by teaching ...
Hogyeong Jeong, Gautam Biswas
INTERSPEECH
2010
13 years 5 months ago
Boosted mixture learning of Gaussian mixture HMMs for speech recognition
In this paper, we propose a novel boosted mixture learning (BML) framework for Gaussian mixture HMMs in speech recognition. BML is an incremental method to learn mixture models fo...
Jun Du, Yu Hu, Hui Jiang
ICIP
2003
IEEE
15 years 10 days ago
Feature selection for unsupervised discovery of statistical temporal structures in video
We present algorithms for automatic feature selection for unsupervised structure discovery from video sequences. Feature selection in this scenario is hard because of the absence ...
Lexing Xie, Shih-Fu Chang, Ajay Divakaran, Huifang...
ACL
2010
13 years 8 months ago
Unsupervised Discourse Segmentation of Documents with Inherently Parallel Structure
Documents often have inherently parallel structure: they may consist of a text and ries, or an abstract and a body, or parts presenting alternative views on the same problem. Reve...
Minwoo Jeong, Ivan Titov