Information Technology Reference
In-Depth Information
Kotthoff, L.; Gent, I. & Miguel I. (2011). A Preliminary Evaluation of Machine Learning in
Algorithm Selection for Search Problems. In: AAAI Publications, Fourth International
Symposium on Combinatorial Search (SoCS) , Borrajo, Daniel and Likhachev, Maxim
and López, Carlos Linare, pp. 84-91, AAAI Press, Retrieved from:
Lagoudakis, M. & Littman, M. (2000). Algorithm Selection Using Reinforcement Learning.
Proceedings of the Sixteenth International Conference on Machine Learning . P. Langley
(Ed.), AAAI Press, pp. 511-518
Lagoudakis, M. & Littman, M. (2001). Learning to select branching rules in the dpll
procedure for satisfiability. Electronic Notes in Discrete Mathematics , Vol. 9, (June
2001), pp. 344-359
Lawler, E.; Lenstra, J.; Rinnooy, K. & Schmoys, D. (1985). The Traveling Salesman Problem: A
Guided Tour of Combinatorial Optimization . John Wiley & Sons, New York, USA
Leyton-Brown, K.; Nudelman, E.; Andrew, G.; McFadden, J. & Shoham, Y. (2003). A
portfolio approach to algorithm selection. Proceedings of International joint conference
on artificial intelligence , Vol. 18, pp. 1542-3
Li, J.; Skjellum, A. & Falgout, R. (1997). A Poly-Algorithm for Parallel Dense Matrix
Multiplication on Two-Dimensional Process Grid Topologies. Concurrency, Practice
and Experience , Vol. 9, No. 5, pp. 345-389
Lobjois, L. & Lemâitre, M. (1998). Branch and bound algorithm selection by performance
prediction. In: AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference
on Artificial intelligence/Innovative applications of artificial intelligence , Jack Mostow,
Charles Rich, Bruce Buchanan, pp. 353-358, AAAI Press, Retrieved from:
Ludermir, T.B.; Ricardo B. C. Prudêncio, R.B.C; Zanchettin, C. (2011). Feature and algorithm
selection with Hybrid Intelligent Techniques. International Journal Hybrid Intelligent
Systems ,Vol. 8, No. 3, pp. 115-116
Madani, O.; Raghavan, H. & Jones, R. (2009). On the Empirical Complexity of Text
Classification Problems. SRI AI Center Technical Report
Malitsky, Y., Sabharwal, A., Samulowitz, H., & Sellmann M. (2011). Non-Model-Based
Algorithm Portfolios for SAT, Proceedings of the 14th international conference on
Theory and Applications of Satisfiability Testing , Ann Arbor, June 2011
Messelis, T.; Haspeslagh, S.; Bilgin, B.; De Causmaecker, P. & Vanden Berghe, G. (2009).
Towards prediction of algorithm performance in real world optimization problems.
Proceedings of the 21st Benelux Conference on Artificial Intelligence , BNAIC, Eindhoven,
pp. 177-183
Michlmayr, E. (2007). Ant Algorithms for Self-Organization in Social Networks . PhD thesis,
Women's Postgraduate College for Internet Technologies (WIT), Vienna, Austria
Nascimento, A. C. A., Prudencio, R. B. C., Costa, I. G., & de Souto, M. C. P. (2009). Mining
rules for the automatic selection process of clustering methods applied to cancer
gene expression data, Proceedings of 19th International Conference on Artificial Neural
Networks (ICANN) , Cyprus, September 2009
Nikolić, M.; Marić, F. & Janičić, P. (2009). Instance-Based Selection of Policies for SAT
Solvers. Lecture Notes in Computer Science, Theory and Applications of Satisfiability
Testing , Vol. 5584, pp. 326-340
O'Mahony, E., Hebrard, E., Holland, A., Nugent, C., & O'Sullivan, B. (2009). Using Case-
based Reasoning in an Algorithm Portfolio for Constraint Solving. (2008).
Search WWH ::

Custom Search