Online Learning of k-CNF Boolean Functions
This paper revisits the problem of learning a k-CNF Boolean function from examples, for fixed k, in the context of online learning under the logarithmic loss. We give a Bayesian interpretation to one of Valiant’s classic PAC learning algorithms, which we then build upon to derive three efficient, online, probabilistic, supervised learning algorithms for predicting the output of an unknown k-CNF Boolean function. We analyze the loss of our methods, and show that the cumulative log-loss...[Show more]
|Collections||ANU Research Publications|
|Source:||Exploiting Symmetries by Planning for a Descriptive Quotient|
|Access Rights:||Open Access|
|01_Veness_Online_Learning_of_k-CNF_2015.pdf||424.85 kB||Adobe PDF|
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