8970 search results - page 123 / 1794 » Learning to Learn Causal Models |
141
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ICANN
2009
Springer
15 years 8 months ago
2009
Springer
Abstract. Many applications of machine learning involve sparse highdimensional data, where the number of input features is (much) larger than the number of data samples, d n. Predi...
148
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ITS
1992
Springer
15 years 8 months ago
1992
Springer
This paper describes a distributed learning system which consists of two connected computers so that students can learn in collaboration and/or competition at different locations. ...
120
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AAAI
1997
15 years 5 months ago
1997
This paper steps back from the standard infinite horizon formulation of reinforcement learning problems to consider the simpler case of finite horizon problems. Although finite ho...
111
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UAI
1997
15 years 5 months ago
1997
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...
131
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AAAI
1996
15 years 5 months ago
1996
This article advocates a new model for inductive learning. Called sequential induction, it helps bridge classical fixed-sample learning techniques (which are efficient but difficu...