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140
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ICML
1999
IEEE
16 years 4 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
150
Voted
KDD
2008
ACM
183views Data Mining» more  KDD 2008»
16 years 3 months ago
Knowledge transfer via multiple model local structure mapping
The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from wh...
Jing Gao, Wei Fan, Jing Jiang, Jiawei Han
143
Voted
DATAMINE
1999
108views more  DATAMINE 1999»
15 years 3 months ago
A Survey of Methods for Scaling Up Inductive Algorithms
Abstract. One of the de ning challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, ...
Foster J. Provost, Venkateswarlu Kolluri
WWW
2010
ACM
15 years 10 months ago
Actively predicting diverse search intent from user browsing behaviors
This paper is concerned with actively predicting search intent from user browsing behavior data. In recent years, great attention has been paid to predicting user search intent. H...
Zhicong Cheng, Bin Gao, Tie-Yan Liu
IVC
2002
148views more  IVC 2002»
15 years 3 months ago
Detecting lameness using 'Re-sampling Condensation' and 'multi-stream cyclic hidden Markov models'
A system for the tracking and classification of livestock movements is presented. The combined `tracker-classifier' scheme is based on a variant of Isard and Blakes `Condensa...
Derek R. Magee, Roger D. Boyle