A neural network model for inter-problem adaptive online time allocation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3697 LNCS, Issue , 2005, Pages 7-12

  Gagliolo, Matteo ^a   Schmidhuber, Jürgen ^a,b  

^a DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE IDSIA   (Switzerland)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; ALGORITHMS; HEURISTIC METHODS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING;

CENSORED SAMPLING; INTRA-PROBLEM HEURISTICS; ONLINE FASHIONS; PARAMETER HAND-TUNING;

NEURAL NETWORKS;

EID: 33646261877     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (12)

1. Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18 (2002) 77-95
2. Schmidhuber, J., Zhao, J., Wiering, M.: Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement. Machine Learning 28 (1997) 105-130 - Based on: Simple principles of metalearning. TR IDSIA-69-96, 1996.
3. Harick, G.R., Lobo, F.G.: A parameter-less genetic algorithm. In Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E., eds.: Proceedings of the Genetic and Evolutionary Computation Conference. Volume 2., Orlando, Florida, USA, Morgan Kaufmann (1999) 1867
4. Lagoudakis, M.G., Littman, M.L.: Algorithm selection using reinforcement learning. In: Proc. 17th International Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA (2000) 511-518
5. Horvitz, E., Ruan, Y., Gomes, C.P., Kautz, H.A., Selman, B., Chickering, D.M.: A bayesian approach to tackling hard computational problems. In: UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, San Francisco, CA, USA, Morgan Kaufmann Publishers Inc. (2001) 235-244
6. Gagliolo, M., Zhumatiy, V., Schmidhuber, J.: Adaptive online time allocation to search algorithms. In Boulicaut, J.F., Esposito, F., Giannotti, F., Pedreschi, D., eds.: Machine Learning: ECML 2004. Proceedings of the 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Springer (2004) 134-143 - Extended tech. report available at http://www.idsia.ch/idsiareport/IDSIA-23-04.ps.gz.
7. Fürnkranz, J., Petrak, J., Brazdil, P., Soares, C: On the use of fast subsampling estimates for algorithm recommendation. Technical Report TR-2002-36, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien (2002)
8. Gomes, C.P., Selman, B.: Algorithm portfolios. Artificial Intelligence 126 (2001) 43-62
9. Nelson, W.: Applied Life Data Analysis. John Wiley, New York (1982)
10. Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press (1995)
11. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
12. Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation 3 (1991) 79-87