Better personalization using learning classifier systems

Proceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005

Volumn , Issue , 2005, Pages 2573-2588

  Shankar, Anil ^a   Louis, Sushil J ^a  

^a Reno   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTEXT-SENSITIVE; GENETICS BASED MACHINE LEARNING; LEARNING CLASSIFIER SYSTEM; PERSONALIZATIONS;

ARTIFICIAL INTELLIGENCE; DECISION TREES;

LEARNING SYSTEMS;

EID: 84872084090     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Motion, http://motion.sourceforge.net/.
2. P. D. Adamczyk and B. P. Bailey. If nor now, when?: the effects of interruption at different moments within task execution. Proceedings of the 2004 conference on Human factors in computing systems, pages 271-278, 2004.
3. A. Black, P. Taylor, and R. Calcv. The festival speech synthesis system. 1998.
4. M. V. Butz. XCSJava 1.0: An Implementation of the XCS classifier system in Java . Technical Report 2000027, Illinois Generic Algorithms Laboratory, 2000.
5. C.M.U.Sphinx: Open source speech recognition. http://www.spcech.cs.cmu. edu/sphinx/.
6. A. K. Dev, G. D. Abowd, and D. Salber. A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware appplications. Human Computer Interaction, 16, 2001.
7. J. Fogarty, S- F. Hudson, and J Lai. Examining the robustness of sensor-based statistical models of human inrerruptibility. Proceedings of the 2004 conferenceon Human factors in computing systems, pages 207-214, 2004.
8. D. F. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989.
9. J. H. Holland. Escaping brittleness: The possibilities of general-purpose learning lgorithms applied to parallel rule-based systems. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach: Volume II, pages 593-623. Kaufmann, Los Altos, CA, 1986.
10. J. H. Holmes. Learning classifier systems applied to knowledge discovery in clinical research databases. In Learning Classifier Systems, From Foundations to Applications, pages 243-262, London, UK, 2000. Springer-Verlag.
11. F. Horvitz and J. Apacible. Learning and reasoning about interruption. Proceedings of the 5th international conference on Multimodal interfaces, pages 20-27, 2003.
12. S. Hudson, J. Fogarty, C. Atkeson, J. Forlizzi, S. Kiesler, J. Lee, and J. Yang. Predicting human interruptibility with sensors: A wizard of oz feasibility study. Proceedings of CHI 2003, ACM Press, 2003.
13. T. Kovacs. XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functionspages 59-68. Springer-Verlag, August 1997.
14. A. Kulkarni. A reactive behavioral system for the intelligent room. Master's thesis, Massachusetts Institute of Technology, Cambridge, MA, 2002., 2002.
15. S. J. Louis and A. Shankar. Context learning can improve user interaction. In Proceedings of the 2004 IFEE International Conference on Information Reuse and Integration, IRI - 2004, November 8-10, 2004, Vegas Hilton, Las Vegas, NV USA, pages 115-120, 2004.
16. J- R Quinlan. C4-5: Programs for Machine Learning. Morgan Kaufmann, 1992.
17. S. Saxon and A. Rarrv. XCS and the monk's problems. In W. Bnnzhaf, J. Daida, A. F.. F.iben, M. H. Garzon, V. Honavar, M. Jakiela, and R. F.. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, page 809, Orlando, Florida, USA, 13-17 1999. Morgan Kaufmann.
18. S. W. Wilson. Classifier systems and the animat problem. Mach. Learn., 2(3): 199-228, 1987.
19. S. W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1 ):1-18, 1994.
20. S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2): 149-170, 1995.
21. S. W. Wilson. Generalization in the XCS classifier system. In J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. F.Goldberg, H. Iba, and R. Riolo, editors, Genetic Programming 1008: Proceedings of the Third Annual Conference, pages 665-674, University of Wisconsin, Madison, Wisconsin, USA, 22-25 1998. Morgan Kaufmann.
22. S. W. Wilson. Mining oblique data with XCS. Lecture Notes in Computer Science, 1996, 2001.
23. I- H. Witten and F. Frank. Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann. San Francisco, USA, 2000.