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ICML
2005
IEEE
14 years 8 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
ICML
1997
IEEE
14 years 8 months ago
Hierarchical Explanation-Based Reinforcement Learning
Explanation-Based Reinforcement Learning (EBRL) was introduced by Dietterich and Flann as a way of combining the ability of Reinforcement Learning (RL) to learn optimal plans with...
Prasad Tadepalli, Thomas G. Dietterich
ETS
2000
IEEE
90views Hardware» more  ETS 2000»
13 years 7 months ago
Role of Contracts in Enhancing Community Building in Web Courses
Project-based work via telecommunications requires the instructor and the students to take explicit steps to create an on-line community that is focused on high quality learning a...
Karen L. Murphy, Sue E. Mahoney, Tina J. Harvell

Publication
151views
12 years 6 months ago
Robust Bayesian reinforcement learning through tight lower bounds
In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinfo...
Christos Dimitrakakis
SIGIR
2012
ACM
11 years 10 months ago
Active query selection for learning rankers
Methods that reduce the amount of labeled data needed for training have focused more on selecting which documents to label than on which queries should be labeled. One exception t...
Mustafa Bilgic, Paul N. Bennett