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Lecture Notes in Computer Science
Over the years a range of positive algorithmic results have been obtained for learning various classes of monotone Boolean functions from uniformly distributed random examples. To date, the only negative result for learning monotone functions in this model is an information-theoretic lower bound showing that certain superpolynomial-size monotone circuits cannot be learned to accuracy 1/2 + ω(log n)/ √ n (Blum et al., FOCS '98). This is in contrast with the situation for non-monotone functions,doi:10.1007/978-3-540-70575-8_4 fatcat:v6zgngwvpzb27jhxalzfytr6d4