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ICML
2001
IEEE
15 years 10 months ago
Continuous-Time Hierarchical Reinforcement Learning
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
Mohammad Ghavamzadeh, Sridhar Mahadevan
ICML
2001
IEEE
15 years 10 months ago
Learning to Select Good Title Words: An New Approach based on Reverse Information Retrieval
In this paper, we show how we can learn to select good words for a document title. We view the problem of selecting good title words for a document as a variant of an Information ...
Rong Jin, Alexander G. Hauptmann
ICML
2001
IEEE
15 years 10 months ago
Bayesian approaches to failure prediction for disk drives
Hard disk drive failures are rare but are often costly. The ability to predict failures is important to consumers, drive manufacturers, and computer system manufacturers alike. In...
Greg Hamerly, Charles Elkan
ICML
2001
IEEE
15 years 10 months ago
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...
ICML
2001
IEEE
15 years 10 months ago
Expectation Maximization for Weakly Labeled Data
We call data weakly labeled if it has no exact label but rather a numerical indication of correctness of the label "guessed" by the learning algorithm - a situation comm...
Yuri A. Ivanov, Bruce Blumberg, Alex Pentland