P. Auer and M. K. Warmuth, Tracking the best disjunction, Proceedings of IEEE 36th Annual Foundations of Computer Science, pp.127-150, 1998.
DOI : 10.1109/SFCS.1995.492487

R. J. Bayardo and J. D. Pehoushek, Counting models using connected components, 17th National Conference on Artificial Intelligence, pp.157-162, 2000.

E. Birnbaum and E. L. Lozinskii, The good old Davis-Putnam procedure helps counting models, Journal of Artificial Intelligence Research, vol.10, pp.457-477, 1999.

M. Chavira and A. Darwiche, On probabilistic inference by weighted model counting, Artificial Intelligence, vol.172, issue.6-7, 2008.
DOI : 10.1016/j.artint.2007.11.002

D. Chawla, L. Li, and S. Scott, On approximating weighted sums with exponentially many terms, Journal of Computer and System Sciences, vol.69, issue.2, pp.196-234, 2004.
DOI : 10.1016/j.jcss.2004.01.006
URL : http://doi.org/10.1016/j.jcss.2004.01.006

K. Crammer, O. Dekel, J. Keshet, S. Shalev-shwartz, and Y. Singer, Online passive-aggressive algorithms, Journal of Machine Learning Research, vol.7, pp.551-585, 2006.

O. Dekel, S. Shalev-shwartz, and Y. Singer, The Forgetron: A kernelbased Perceptron on a fixed budget, Advances in Neural Information Processing Systems 18, 2005.

C. Domshlak and J. Hoffmann, Probabilistic planning via heuristic forward search and weighted model counting, Journal of Artificial Intelligence Research, vol.30, pp.565-620, 2007.

J. Fürnkranz, Separate-and-conquer rule learning, Artificial Intelligence Review, vol.13, issue.1, pp.3-54, 1999.
DOI : 10.1023/A:1006524209794

R. Khardon, D. Roth, and R. A. Servedio, Efficiency versus convergence of boolean kernels for on-line learning algorithms, Journal of Artificial Intelligence Research, vol.24, pp.341-356, 2005.

R. Khardon and R. A. Servedio, Maximum Margin Algorithms with Boolean Kernels, J. of Machine Learning Res, vol.6, pp.1405-1429, 2005.
DOI : 10.1007/978-3-540-45167-9_8

N. Littlestone, Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm, Machine Learning, pp.285-318, 1988.

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, pp.386-408, 1958.
DOI : 10.1037/h0042519

K. Sadohara, Learning of Boolean Functions Using Support Vector Machines, 12th Int. Conference on Algorithmic Learning Theory, pp.106-118, 2001.
DOI : 10.1007/3-540-45583-3_10

T. Sang, F. Bacchus, P. Beame, H. A. Kautz, and T. Pitassi, Combining component caching and clause learning for effective model counting, 7th Int. Conference on Theory and Applications of Satisfiability Testing, 2004.

T. Sang, P. Beame, and H. A. Kautz, Performing Bayesian inference by weighted model counting, 20th National Conference on Artificial Intelligence, pp.475-482, 2005.

J. C. Schlimmer and R. H. Granger, Beyond incremental processing: Tracking concept drift, 5th National Conference on Artificial Intelligence, pp.502-507, 1986.

M. Thurley, sharpSAT ??? Counting Models with Advanced Component Caching and Implicit BCP, 9th Int. Conference on Theory and Applications of Satisfiability Testing, pp.424-429, 2006.
DOI : 10.1007/11814948_38
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.84.697