@techreport{
 title = {Feature Discovery for Inductive Concept Learning 1 Introduction},
 type = {techreport},
 year = {1990},
 websites = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.9509&rep=rep1&type=pdf},
 city = {Amherst, MA},
 institution = {University of Massachusetts at Amherst},
 department = {Department of Computer Science},
 id = {13465b40-4673-3b24-b4ec-c7639d852592},
 created = {2009-08-18T04:22:15.000Z},
 file_attached = {false},
 profile_id = {7edb4dd2-367e-3b20-8e26-a7e1d668fa33},
 last_modified = {2017-10-14T18:03:58.700Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Fawcett:90},
 source_type = {Tech Report},
 private_publication = {false},
 abstract = {This paper describes Zenith, a discovery system that performs constructive induction. The system is able to generate and extend new features for concept learning using agenda-based heuristic search. The search is guided by feature worth (a composite measure of discriminability and cost). Zenith is distinguished from existing constructive induction systems by its interaction with a performance system, and its ability to extend its knowledge base by creating new domain classes. Zenith is able to discover known useful features for the Othello board game.},
 bibtype = {techreport},
 author = {Fawcett, Tom E}
}

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