6 Result(s)
within
Ryszard S. Michalski
Computer Science
Database Management
Information Storage and Retrieval
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Chapter and Conference Paper
Learning from inconsistent and noisy data: The AQ18 approach
In concept learning or data mining tasks, the learner is typically faced with a choice of many possible hypotheses characterizing the data. If one can assume that the training data are noise-free, then the gen...
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Chapter and Conference Paper
Inductive Databases and Knowledge Scouts
The development of very large databases and the world wide web has created extraordinary opportunities for monitoring, analyzing and predicting global economical, ecological, demographic, political, and other ...
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Chapter and Conference Paper
A Knowledge Scout for Discovering Medical Patterns: Methodology and System SCAMP
Knowledge scouts are software agents that autonomously synthesize knowledge of interest to a given user (target knowledge) by applying inductive database operators to a local or distributed dataset. This paper...
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Chapter
Learning Patterns in Noisy Data: The AQ Approach
In concept learning and data mining, a typical objective is to determine concept descriptions or patterns that will classify future data points as correctly as possible. If one can assume that the data contain...
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Chapter and Conference Paper
Incremental Learning with Partial Instance Memory
Agents that learn on-line with partial instance memory reserve some of the previously encountered examples for use in future training episodes. We extend our previous work by combining our method for selecting...
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Chapter and Conference Paper
Learning and Evolution: An Introduction to Non-darwinian Evolutionary Computation
The field of evolutionary computation has drawn inspiration from Darwinian evolution in which species adapt to the environment through random variations and selection of the fittest. This type of evolutionary ...
