Machine Learning for Cognitive Networks: Technology Assessment and Research Challenges

Cognitive Networks: Towards Self-Aware Networks

Volumn , Issue , 2007, Pages 97-120

  Dietterich, Thomas G ^a   Langley, Pat ^b  

^a OREGON STATE UNIVERSITY   (United States)

^b Institute for the Study of Learning and Expertise   (United States)

Author keywords

Anomaly detection and fault diagnosis; classification or regression; Cognitive approaches to networking; constraint logic programming (CLP); Knowledge plane; Machine learning for cognitive networks; One class learning; pattern completion or flexible prediction

Indexed keywords

EID: 58249123031     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470515143.ch5     Document Type: Chapter

References (48)

1. Aha, D.W., Kibler, D. and Albert, M.K. (1991) Instance-based learning algorithms, Machine Learning, 6, 37.
2. Baluja, S. and Caruana, R. (1995) Removing the genetics from the standard genetic algorithm. Proceedings of the 12th Annual Conference on Machine Learning, p. 38-46, San Francisco.
3. Blake, C.L. and Merz, C.J. (1998) UCI repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html.
4. Blum, A. and Mitchell, T. (1998) Combining labeled and unlabeled data with co-training. Proceedings of the 11th Annual Conference on Computing Learning Theory, pp. 92-100, New York.
5. Boyan J. and Moore, A. (2000) Learning evaluation functions to improve optimization by local search. Journal of Machine Learning Research, 1, 77-112.
6. Boyan, J.A. and Littman, M.L. (1994) Packet routing in dynamically changing networks: a reinforcement learning approach. In J.D. Cowan, G. Tesauro and J. Alspector (eds.), Advances in Neural Information Processing Systems, volume 6, pp. 671-8, Morgan Kaufmann.
7. Buntine, W. (1996) A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering, 8, 195-210.
8. Cheeseman, P., Self, M., Kelly, J., Taylor, W., Freeman, D. and Stutz, J. (1988) Bayesian classification, Proceedings of the Seventh National Conference on Artificial Intelligence, pp. 607-11, St.Paul, MN.
9. Clark, D. (2002) A new vision for network architecture, http://www.isi.edu/braden/know-plane/DOCS/DDCknowledgePlane3.ps.
10. Clark, D.D., Partridge, C., Ramming, J.C. and Wroclawski, J.T. (2003) A knowledge plane for the internet, Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 3-10, Karlsruhe, Germany.
11. Clark, P. and Niblett, T. (1988) The CN2 induction algorithm. Machine Learning, 3, 261.
12. Cristianini, N. and Shawe-Taylor, J. (2000) An Introduction to Support Vector Machines (and other kernelbased learning methods), Cambridge University Press.
13. Cypher, A. (1993) Watch What I Do: Programming by Demonstration, MIT Press, Cambridge, MA.
14. Dechter, R. (2003) Constraint Processing, Morgan Kaufmann, San Francisco.
15. DeJong, G. (2006) Toward robust real-world inference: a new perspective on explanation-based learning. In J. Furnkranz, T. Scheffer and M. Spiliopoulou (eds.), Machine Learning: ECML 2006; Lecture Notes in Computer Science, pp. 102-13, Springer Verlag, Berlin.
16. Drastal, G., Meunier, R. and Raatz, S. (1989) Error correction in constructive induction. In A.Maria Segre (ed.), Proceedings of the Sixth International Workshop on Machine Learning, pp. 81-3, Ithaca, New York.
17. Fisher, D.H. (1987) Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139.
18. Flann, N.S. and Dietterich, T.G. (1989) A study of explanation-based methods for inductive learning. Machine Learning, 4, 187-226.
19. Jaffar, J. and Maher, M.J. (1994) Constraint logic programming: a survey. Journal of Logic Programming, 19/20, 503-81.
20. Kibler, D. and Langley, P. (1988) Machine learning as an experimental science. Proceedings of the 3rd European Working Session on Learning, pp. 81-92, Glasgow.
21. Kumar, V. (1992) Algorithms for constraints satisfaction problems: a survey. AI Magazine, 13, 32-44.
22. Langley, P. (1995) Elements of Machine Learning, Morgan Kaufmann, San Francisco.
23. Langley, P. (1996) Relevance and insight in experimental studies. IEEE Expert, 11-12.
24. Langley, P. (1999) User modeling in adaptive interfaces. Proceedings of the 7th International Conference on User Modeling, pp. 357-70, Banff, Alberta.
25. Langley, P. and Simon, H.A. (1995) Applications of machine learning and rule induction. Communications of the ACM, 38, 55-64.
26. Merz, C.J. and Murphy, P.M. (1996) UCI repository of machine learning databases, http://www.ics.uci. edu/~mlearn/MLRepository.html.
27. Mitchell, T.M. (1997) Machine Learning, McGraw-Hill, New York.
28. Mitchell, T.M., Mahadevan, S. and Steinberg, L.I. (1985) LEAP: a learning apprentice for VLSI design. Proceedings of the 9th International Joint Conference on Artificial Intelligence, pp. 573-80, Los Angeles, CA.
29. Moore, A., Schneider, J., Boyan, J. and Soon Lee, M. (1998) Q2: Memory-based active learning for optimizing noisy continuous functions. Proceedings of the 15th International Conference of Machine Learning, pp. 386-94.
30. Moriarty, D.E., Schultz, A.C. and Grefenstette, J.J. (1999) Evolutionary algorithms for reinforcement learning. Journal of Artificial Intelligence Research, 11, 241-76.
31. Murphy, P.M. and Aha, D.W., (1994) UCI repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html.
32. Myers, R.H. and Montgomery, D.C. (1995) Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, Inc., New York.
33. Oblinger, D., Castelli, V. and Bergman, L. (2006) Augmentation-based learning. IUI2006: 2006 International Conference on Intelligent User Interfaces, pp. 202-9.
34. Ourston, D. and Mooney, R. (1990) Changing the rules: a comprehensive approach to theory refinement. Proceedings of the 8th National Conference on Artificial Intelligence, pp. 815-20.
35. Pack Kaelbling, L., Littman, M.L. and Moore, A.W. (1996) Reinforcement learning: a survey. Journal of Artificial Intelligence Research, 4, 237-85.
36. Partridge, C., (2003) Thoughts on the structure of the knowledge plane, http://www.isi.edu/~braden/knowplane/ DOCS/craig.knowplane.pdf.
37. Priebe, C.E. and Marchette, D.J. (1993) Adaptive mixture density estimation. Pattern Recognition, 26(5), 771-85.
38. Quinlan, J.R. (1993) C4.5: Programs for Empirical Learning, Morgan Kaufmann, San Francisco.
39. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning internal representations by error propagation. In D.E. Rumelhart, J.L. McClelland and the PDP research group (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, MIT Press, Cambridge, MA.
40. Sammut, C., Hurst, S., Kedzier, D. and Michie, D. (1992) Learning to Fly. Proceedings of the 9th International Conference on Machine Learning, pp. 385-93, Aberdeen.
41. Selman, B., Kautz, H.A. and Cohen, B. (1993) Local search strategies for satisfiability testing. Proceedings of the Second DIMACS Challange on Cliques, Coloring, and Satisfiability, Providence RI.
42. Sleeman, D., Langley, P. and Mitchell, T. (1982) Learning from solution paths: an approach to the credit assignment problem, AI Magazine, 3, 48-52.
43. Stolcke, A. and Omohundro, S. (1994) Inducing probabilistic grammars by Bayesian model merging. In R.C. Carrasco and J. Oncina (eds.), Grammatical Inference and Applications: Proceedings of the Second International Colloquium on Grammatical Inference, pp. 106-18, Springer Verlag.
44. Sutton, R. and Barto, A.G. (1998) Introduction to Reinforcement Learning, MIT Press, Cambridge, MA.
45. Tonge, F.M. (1963) Summary of a heuristic line balancing procedure. In E.A. Feigenbaum and J. Feldman (eds.), Computers and Thought, pp. 168-90, AAAI Press/MIT Press, Menlo Park, CA.
46. Williams, R.J. (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229.
47. Zhang, W. and Dietterich, T.G. (1995) A reinforcement learning approach to job-shop scheduling. International Joint Conference on Artificial Intelligence, pp. 1114-20, Montreal, Canada.
48. Zweben, M., Daun, B. and Deale, M. (1994) Scheduling and rescheduling with iterative repair. In M. Zweben and M.S. Fox (eds.), Intelligent Scheduling, pp. 241-55, Morgan Kaufmann, San Francisco.