2 Learning Monotone Boolean Functions
نویسنده
چکیده
Last time we finished the discussion about KM algorithm and its application. We also covered sparse Fourier representations and k-juntas of parities. In the end we started to talk about learning monotone Boolean functions and influence of such functions. Today We will first finish discussion about learning monotone Boolean functions. Then we will also talk about learning k-juntas of halfspaces. We will discuss the behavior of KM algorithm and LMN algorithm on such problems, and also talk about learning results with membership query access and uniform assumption.
منابع مشابه
On Learning Monotone Boolean Functions
Abstract We consider the problem of learning monotone Boolean functions over under the uniform distribution. Specifically, given a polynomial number of uniform random samples for an unknown monotone Boolean function , and given polynomial computing time, we would like to approximate as well as possible. We describe a simple algorithm that we prove achieves error at most , improving on the previ...
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We consider exact learning or identification of monotone Boolean functions by only using membership queries. It is shown that almost all monotone Boolean functions are polynomially identifiable in the input number of variables as well as the output being the sum of the sizes of the CNF and DNF representations. 2001 Elsevier Science B.V. All rights reserved.
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