NeuroCOLT

Neural Networks and Computational Learning Theory

 

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NeuroCOLT Technical Report NC-TR-95-013

Learnability of Kolmogorov-Easy Circuit Expressions Via Queries

Josè L. Balcazar
Universitat Politecnica de Catalunya

Harry Buhrman
CWI, Amsterdam

Montserrat Hermo
Universidad del Païs Vasco

Abstract
Circuit expressions were introduced to provide a natural link between Computational Learning and certain aspects of Structural Complexity. Upper and lower bounds on the learnability of circuit expressions are known. We study here the case in which the circuit expressions are of low (time-bounded) Kolmogorov complexity. We show that these are polynomial-time learnable from membership queries in the presence of an NP oracle. We also exactly characterize the sets that have such circuit expressions, and precisely identify the subclass whose circuit expressions can be learned from membership queries alone. The extension of the results to various Kolmogorov complexity bounds is discussed.

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