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An Application of Codes to Attribute-Efficient Learning

Thomas HofmeisterContact Information

(3)  Informatik 2, Universität Dortmund, D, 44221 Dortmund, Germany
Abstract
We design asymptotically optimal query strategies for the class of parity functions which contain at most k essential variables. The number of questions asked is at most twice the number asked by an optimal strategy. The strategy presented is even non-adaptive. For fixed k, the number of questions is optimal up to additive constants. Our results improve upon results by Uehara, Tsuchida and Wegener [6].

Contact Information Thomas Hofmeister
Email: hofmeist@Ls2.cs.uni-dortmund.de
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