Structural Results About On-line Learning Models With and Without Queries

作者:Peter Auer, Philip M. Long

摘要

We solve an open problem of Maass and Turán, showing that the optimal mistake-bound when learning a given concept class without membership queries is within a constant factor of the optimal number of mistakes plus membership queries required by an algorithm that can ask membership queries. Previously known results imply that the constant factor in our bound is best possible.

论文关键词:computational learning theory, learning with queries, mistake bounds, function learning, learning with noise

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论文官网地址:https://doi.org/10.1023/A:1007614417594