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UAI
1997
13 years 9 months ago
Exploring Parallelism in Learning Belief Networks
It has been shown that a class of probabilistic domain models cannot be learned correctly by several existing algorithms which employ a single-link lookahead search. When a multil...
Tongsheng Chu, Yang Xiang
AAAI
2008
13 years 10 months ago
POIROT - Integrated Learning of Web Service Procedures
POIROT is an integration framework for combining machine learning mechanisms to learn hierarchical models of web services procedures from a single or very small set of demonstrati...
Mark H. Burstein, Robert Laddaga, David McDonald, ...
ICASSP
2011
IEEE
12 years 11 months ago
Sparse coding and dictionary learning based on the MDL principle
The power of sparse signal coding with learned overcomplete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical infe...
Ignacio Ramírez, Guillermo Sapiro
ECML
2005
Springer
14 years 1 months ago
Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another
We present a method for transferring knowledge learned in one task to a related task. Our problem solvers employ reinforcement learning to acquire a model for one task. We then tra...
Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richa...
ECTEL
2010
Springer
13 years 6 months ago
Learning from Erroneous Examples: When and How Do Students Benefit from Them?
We investigate whether erroneous examples in the domain of fractions can help students learn from common errors of other students presented in a computer-based system. Presenting t...
Dimitra Tsovaltzi, Erica Melis, Bruce M. McLaren, ...