TY - GEN
T1 - Online learning of k-CNF boolean functions
AU - Veness, Joel
AU - Hutter, Marcus
AU - Orseau, Laurent
AU - Bellemare, Marc
PY - 2015
Y1 - 2015
N2 - 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 can be upper bounded by a polynomial function of the size of each example.
AB - 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 can be upper bounded by a polynomial function of the size of each example.
UR - http://www.scopus.com/inward/record.url?scp=84949883978&partnerID=8YFLogxK
M3 - Conference contribution
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 3865
EP - 3873
BT - IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
A2 - Wooldridge, Michael
A2 - Yang, Qiang
PB - International Joint Conferences on Artificial Intelligence
T2 - 24th International Joint Conference on Artificial Intelligence, IJCAI 2015
Y2 - 25 July 2015 through 31 July 2015
ER -