Shifting Vocabulary Bias in Speedup Learning

Authors:
Devika Subramanian
Affiliations:
Computer Science Department, Cornell University, Ithaca, NY 14853. devika@cs.cornell.edu
Venue:
Machine Learning - Special issue on bias evaluation and selection
Year:
1995

Citing 0
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Abstract

In this paper, we describe a domain-independent principle for justified shifts of vocabulary bias in speedup learning. This principle advocates the minimization of wasted computational effort. It explains as well as generates a special class of granularity shifts. We describe its automation for definite as well as stratified Horn theories, and present an implementation for a general class of reachability computations.