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NIPS
1992
13 years 9 months ago
Hidden Markov Model} Induction by Bayesian Model Merging
This paper describes a technique for learning both the number of states and the topologyof Hidden Markov Models from examples. The inductionprocess starts with the most specific m...
Andreas Stolcke, Stephen M. Omohundro
SDM
2011
SIAM
183views Data Mining» more  SDM 2011»
12 years 10 months ago
Nonparametric Bayesian Co-clustering Ensembles
A nonparametric Bayesian approach to co-clustering ensembles is presented. Similar to clustering ensembles, coclustering ensembles combine various base co-clustering results to ob...
Pu Wang, Kathryn B. Laskey, Carlotta Domeniconi, M...
LEGE
2003
94views Education» more  LEGE 2003»
13 years 9 months ago
Dynamic Learning Agents and Enhanced Presence on the Grid
Human Learning on the Grid will be based on the synergies between advanced software and Human agents. These synergies will be possible to the extent that conversational protocols ...
Stefano A. Cerri, Marc Eisenstadt, Clement Jonquet
AAAI
1994
13 years 9 months ago
Learning to Coordinate without Sharing Information
Researchers in the eld of Distributed Arti cial Intelligence (DAI) have been developing e cient mechanisms to coordinate the activities of multiple autonomous agents. The need for...
Sandip Sen, Mahendra Sekaran, John Hale
CVPR
1999
IEEE
14 years 10 months ago
Vision-Based Speaker Detection Using Bayesian Networks
The development of user interfaces based on vision and speech requires the solution of a challenging statistical inference problem: The intentions and actions of multiple individu...
James M. Rehg, Kevin P. Murphy, Paul W. Fieguth