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NN
1997
Springer
174views Neural Networks» more  NN 1997»
15 years 6 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
COLT
1994
Springer
15 years 6 months ago
Learning Probabilistic Automata with Variable Memory Length
We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic nite automata...
Dana Ron, Yoram Singer, Naftali Tishby
UIST
1994
ACM
15 years 6 months ago
Extending a Graphical Toolkit for Two-handed Interaction
Multimodal interaction combines input from multiple sensors such as pointing devices or speech recognition systems, in order to achieve more fluid and natural interaction. Twohand...
Stéphane Chatty
105
Voted
CIVR
2006
Springer
139views Image Analysis» more  CIVR 2006»
15 years 6 months ago
Finding People Frequently Appearing in News
Abstract. We propose a graph based method to improve the performance of person queries in large news video collections. The method benefits from the multi-modal structure of videos...
Derya Ozkan, Pinar Duygulu
107
Voted
ECML
2006
Springer
15 years 6 months ago
PAC-Learning of Markov Models with Hidden State
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical ap...
Ricard Gavaldà, Philipp W. Keller, Joelle P...