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ML
1998
ACM
131views Machine Learning» more  ML 1998»
13 years 11 months ago
Learning from Examples and Membership Queries with Structured Determinations
It is well known that prior knowledge or bias can speed up learning, at least in theory. It has proved di cult to make constructive use of prior knowledge, so that approximately c...
Prasad Tadepalli, Stuart J. Russell
ML
1998
ACM
220views Machine Learning» more  ML 1998»
13 years 11 months ago
Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments
Abstract. Coordination is an essential technique in cooperative, distributed multiagent systems. However, sophisticated coordination strategies are not always cost-effective in all...
Toshiharu Sugawara, Victor R. Lesser
ML
1998
ACM
136views Machine Learning» more  ML 1998»
13 years 11 months ago
Co-Evolution in the Successful Learning of Backgammon Strategy
Following Tesauro’s work on TD-Gammon, we used a 4000 parameter feed-forward neural network to develop a competitive backgammon evaluation function. Play proceeds by a roll of t...
Jordan B. Pollack, Alan D. Blair
ML
1998
ACM
113views Machine Learning» more  ML 1998»
13 years 11 months ago
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
During a project examining the use of machine learning techniques for oil spill detection, we have encountered several essential questions that we believe deserve the attention of ...
Miroslav Kubat, Robert C. Holte, Stan Matwin
ML
1998
ACM
115views Machine Learning» more  ML 1998»
13 years 11 months ago
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
This paper is a case study of a machine aided knowledge discovery process within the general area of drug design. More speci cally, the paper describes a sequence of experiments in...
Paul W. Finn, Stephen Muggleton, David Page, Ashwi...
ML
1998
ACM
139views Machine Learning» more  ML 1998»
13 years 11 months ago
The Hierarchical Hidden Markov Model: Analysis and Applications
We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our m...
Shai Fine, Yoram Singer, Naftali Tishby
ML
1998
ACM
101views Machine Learning» more  ML 1998»
13 years 11 months ago
Elevator Group Control Using Multiple Reinforcement Learning Agents
Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest. RL algorithmshave appeared that approximatedynamic programming on an ...
Robert H. Crites, Andrew G. Barto
ML
1998
ACM
13 years 11 months ago
On Restricted-Focus-of-Attention Learnability of Boolean Functions
In the k-Restricted-Focus-of-Attention (k-RFA) model, only k of the n attributes of each example are revealed to the learner, although the set of visible attributes in each example...
Andreas Birkendorf, Eli Dichterman, Jeffrey C. Jac...
ML
2002
ACM
146views Machine Learning» more  ML 2002»
13 years 11 months ago
Kernel Matching Pursuit
Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target fu...
Pascal Vincent, Yoshua Bengio