Keywords
1. ANN - Artificial Neural Networks, Neurocomputing, Connectionist Approaches
2. BBN – Bayesian Belief Networks, Probabilistic Reasoning
3. CBR – Case-Based Reasoning, Reasoning by Analogy
4. CF – Certainty Factors, Dempster-Shafer Theory, Belief-Disbelief, Evidence Theory, Approximate Reasoning, Uncertainty Reasoning
5. CI /SC - Computational Intelligence, Soft-Computing
6. CKA / IKA - Theory Refinement, Constructive Knowledge Acquisition, Incremental Machine Learning Tools, Incremental Knowledge Acquisition
7. DFA – Discrete Finite State Automata
8. EC / GA / GP - Evolutionary Computing, Genetic Algorithms, Genetic Programming
9. EBL - Explanation-Based Learning
10. FES - Fuzzy-Logic, Fuzzy Systems, Fuzzy Inference Systems, Fuzzy Expert Systems
11. FNS - Fuzzy Neural Systems, Neuro-Fuzzy Systems
12. HNSS / KBNC / KBANN - Hybrid Neural Systems, Hybrid Neuro-Symbolic Systems, Neuro-Symbolic Integration, Integration of Connectionist and Symbolic Processing, Knowledge-Based Neurocomputing, Knowledge-Based Artificial Neural Networks, Fusion of Computational and Symbolic Processing
13. HS - Hybrid Systems, Hybrid Intelligent Systems
14. IDT / ID3 / C4.5 /CART – Induction of Decision Trees, Regression Trees
15. ILP – Inductive Logic Programming
16. KBS / ES – Knowledge-Based Systems, Expert Systems, Rule-Based Systems, Production Systems
17. KDD – Knowledge Data Discovery, Data Mining
Books
1. [Andrews96] Andrews, R. & Diederich, J. (Eds.). Rules and networks. QUT - Queensland University of Technology. Brisbane, Austrália, 1996.
2. [Arbib95] Arbib, M. A. (Ed.). The Handbook of Brain Theory and Neural Networks. MIT Press, 1995.
3. [Cloete00] Cloete, I. & Zurada, J. (Eds.). Knowledge-Based Neurocomputing. MIT Press, Cambridge, MA. 2000.
4. [Davis91] Davis, L. Handbook of Genetic Algorithms. Van Nostrand Reinhold. 1991.
5. [Eberhart] Eberhart, R. C., Dobbins, R. C., & Simpson, P. K. Computational Intelligence PC Tools. Morgan Kaufmann Publishers. Reading: Academic Press Professional, 1990.
6. [Fiesler96] Fiesler, E. & Bearle, R. Handbook of Neural Computation. Oxford University Press – IOP Publishing. 1996.
7. [Fu94] Fu, L. Neural Networks in Computer Intelligence. McGraw-Hill Publishing Inc., 1994.
8. [Furuhashi01] Furuhashi, Y., Tano, S., & Jacobsen, H. A. Deep Fusion of Computational and Symbolic Processing. Physica-Verlag, Studies in Fuzziness and Soft Computing Series, vol.59. Heidelberg, 2001.
9. [Giarratano98] Giarratano, J. & Riley, G. Expert Systems: Principles and Programming. 3rd Edition, PWS Publishing, Boston, MA. 1998.
10. [Golberg89] Golberg, D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley. 1989.
11. [Kandel 92] Kandel, A. & Langholz, G. (Eds.). Hybrid Architectures for Intelligent Systems. CRC Press, Boca Raton - Florida, 1992.
12. [Kolodner93] Kolodner J. Case-based reasoning. Morgan Kaufmann, San Mateo. Representation and Reasoning Series, 1993.
13. [Medsker 94] Medsker, L. R. Hybrid Neural Network and Expert Systems. Kluwer Academic Publishers, Boston. 1994.
14. [Mitchell97] Mitchell, T. M. Machine Learning. McGraw-Hill Publishing Company, McGraw-Hill Series in Computer Science (Artificial Intelligence). 1997.
15. [Nikolopoulos97] Nikolopoulos, C. Expert Systems - Introduction to First and Second Generation and Hybrid Knowledge Based Systems. Marcel Dekker Inc. Press, 1997.
16. [Nilsson98] Nilsson, N. J. Artificial Intelligence: a new sinthesys. Morgan Kauffman, San Mateo, CA. 1998.
17. [Quinlan93] Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann. Publishers, San Mateo, CA - U.S.A. 1993.
18. [Rumelhart86] Rumelhart, D. & McClelland, J. L. (Eds.). Parallel Distributed Processing –Explorations in the Microstructure of Cognition - Vol.1: Foundations, Vol.2: Psychological and Biological Models. Cambridge: MIT Press, 1986.
19. [Sun97] Sun, R. & Alexandre, F. (Eds.). Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Lawrence Erlbaum Associates, 1997.
20. [Wermter00] Wermter, S. & Sun, R. (Eds.). Hybrid Neural Systems. Springer, Heidelberg. 2000.
Thesis, Papers, Journals, Conferences, and Book Chapters
1. [Andrews95] Andrews, R., Diederich, J., & Tickle, A. B. A Survey And Critique of Techniques For Extracting Rules From Trained ANN. Technical Report - Neurocomputing Research Centre - QUT (Queensland University of Technology, Brisbane - Australia, January 1995). Also published in: Knowledge-Based Systems 8(6), p.378-38 (1995). (or ftp://ftp.fit.qut.edu.au /pub/NRC/tr/ps/QUTNRC-95-01-02.ps.Z)
2. [Bologna00] Bologna, G. Symbolic Rule Extraction from the DIMLP Neural Network. Hybrid Neural Systems. Stefan Wermter and Ron Sun (Eds.). Springer, Heidelberg. 2000.
3. [Bologna00a] Bologna, G. Rule Extraction from a Multi-Layer Perceptron with Staircase Activation Functions. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00). Como, Italy. 2000.
4. [Cechin96] Cechin, A., Epperlein, U., Koppenhoefer, B., & Rosenstiel, W. The Extraction of Sugeno Fuzzy Rules from Neural Networks. in Michel Verleysen (Ed.): Proceedings of the European Symposium on Artificial Neural Networks, 49-54, Brussels, Belgium, 1996. D facto publications.
5. [Cechin98] Cechin, A. The Extraction of Fuzzy Rules from Neural Networks. Ph.D. Thesis. Fakultät für Informatik der Eberhard-Karls-Universität zu Tübingen, Shaker Verlag, Aachen, ISBN 3-8265-3541-3, 1998.
6. [Decloedt96] Decloedt, L., Osorio, F. S., & Amy, B. Rule_Out Method: A New Approcah for Knowledge Explicitation from Trained ANN. Andrews, Robert, Diederich, Joachim. (Eds.) In: Rules and networks. Queensland University - Brisbane - Austrália, 1996, v.1, p.25-33.
7. [DeJong88] De Jong, K. A. Learning with Genetic Algorithms: An Overview. Machine Learning, 13, pp.121-138. Kluwer Academic Publishers, Boston, MA - U.S.A. 1988.
8. [DeJong90] De Jong, K. A. Genetic Algorithm-Based Learning. In: Machine Learning - Vol.3. Y. Kodratoff et al. (Eds.), Chapter 21. Morgan Kaufmann Publishers, San Mateo. 1990.
9. [Feigenbaum82] Feigenbaum, E. Knowledge Engineering in the 1980s. Dept. of Computer Science. Stanford University, Stanford, CA. 1982.
10. [Fu93] Fu, L. Knowledge-Based Connectionism for Revising Domain Theories. IEEE Transactions on Systems, Man and Cybernetics, Vol.23, N.1, January/February 1993.
11. [Fu94a] Fu, L. Rule Creation from Neural Networks. IEEE Transactions on Systems, Man and Cybernetics, Vol.24, N.8. August 1994.
12. [Gallant88] Gallant, S. I. Connectionist Expert Systems. Communications of the ACM, 31(2):152-169.
13. [Gallant95] Gallant, S. I. Expert Systems and Decision Systems Using Neural Networks. In: The Handbook of Brain Theory and Neural Networks. M. Arbib (Ed.), MIT Press, pp.377-380.1995.
14. [Giacometti92] Giacometti, A. Modèles hybrides de l'expertise. Ph.D. Thesis - Thèse de Doctorat, LIFIA - IMAG (Grenoble - France, 1992). (or ftp://ftp.imag.fr/pub/LEIBNIZ/RESEAUX-D-AUTOMATES/giacometti.these.ps.tar.gz)
15. [Hilario 97] Hilario, M. An overview of Strategies for Neurosymbolic Integration. In: Sun, Ron & Alexandre, Frederic (Eds.). Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Chapter 2. Lawrence Erlbaum Associates, 1997.
16. [Hilario94] Hilario, M. MIX: Modular Integration of Connectionist and Symbolic Processing in Knowledge-Based Systems. Proposal for Basic Research Project - Esprit BRA, EEC, n° 09119.
17. [Hilario96] Hilario, M. An Overview of Strategies for Neurosymbolic Integration. In: Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Ron Sun (Ed.) – Chapter 2. (Kluwer Academic Publishers, 1996).
18. [Lu95] Lu, H., Setiono, R., & Liu, H. NeuroRule: a connectionist approach to data mining. In Proceedings of 21st International Conference on Very Large Data Bases, Zurich, Switzerland, September 1995, pages 478-489.
19. [Machado] Machado, R. J. & Freitas da Rocha, A. Evolutive fuzzy neural networks. In: Proceedings of the 1992 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 493--500, San Diego, CA, 8.-12. March 1992. IEEE, New York.
20. [Mahoney93] Mahoney, J. J. & Mooney, R. Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases. Connection Science, 5(1993), pp.339-364. 1993. (or ftp://ftp.cs.utexas.edu/pub/mooney/papers/)
21. [Mahoney96] Mahoney, J. J. Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule Bases. Ph.D. Thesis, Dept. of Computer Science, Univ. of Texas at Austin, May 1996. (or ftp://ftp.cs.utexas.edu/pub/mooney/papers/ rapture-dissertation-96.ps.Z)
22. [Malek95] Malek, M. & Amy, B. Preprocessing Model for Integrating CBR and Prototype-Based Neural Networks. In: Proceedings of IJCAI Workshop - Connectionist Symbolic Integration: from Unified to Hybrid Approaches, Montreal. 1995.
23. [Malek96] Malek, M. Un modèle hybride de mémoire pour le raisonnement à partir de cas. Ph.D. Thesis - Thèse de Doctorat, UJF - LEIBNIZ (Grenoble - France, 1996). (or ftp://ftp.imag.fr/pub/LEIBNIZ/RESEAUX-D-AUTOMATES/malek.these.ps.gz)
24. [Minski90] Minsky, M. Logical vs. Analogical or Symbolic vs. Connectionist or Neat vs. Scruffy. In: Artificial Intelligence at MIT - Expanding Frontiers, P. Winston (Ed.), vol.1, MIT Press (reprinted in AI Magazine). 1990.
25. [Osorio00] Osorio, F. S., Amy, B., & Cechin, A. Hybrid Machine Learning Tools - INSS: A Neuro-Symbolic System for Constructive Machine Learning. Furuhashi, T., Tano, S., Jacobsen, H.-A. (Eds.) In: Deep fusion of computational and symbolic processing. Springer Verlag: Berlin, 2000, v.1, p.1-23.
26. [Osorio98] Osorio, F. S. INSS: Un Système Hybride Neuro-Symbolique pour l'Apprentissage Automatique Constructif. Ph.D. Thesis - Thèse de Doctorat, INPG - Laboratoire LEIBNIZ - IMAG (Grenoble, France, 1998). (or ftp://ftp.imag.fr/pub/LEIBNIZ/RESEAUX-D-AUTOMATES/osorio.these.ps.gz)
27. [Osorio98a] Osorio, F. S. & Amy, B. INSS - A Hybrid System for Constructive Machine Learning. NEURAP'98 – International Conference on Neural Applications. 1998, Marselha - França. p.369-376.
28. [Osorio99] Osorio, F. S. & Amy, B. INSS: A Hybrid System for Constructive Machine Learning. Neurocomputing, Elsevier Press. Netherlands, v.1999, n.28, p.191-205, 1999.
29. [Pollack90] Pollack, J. B. Recursive Distributed Representations. Artificial Intelligence, 46:77-105.
30. [Setiono00] Setiono, R., Leow, W. K., & Thong, J.Y-L. Opening the neural network blackbox: An algorithm for extracting rules from function approximating neural networks. In Proceedings of ICIS 2000, International Conference on Information Systems, Brisbane, Australia, December 10 - 13, 2000.
31. [Setiono00a] Setiono, R. & Leow, W. K. FERNN: An algorithm for Fast Extraction of Rules from Neural Networks. Applied Intelligence, 2000, Vol. 12, No. 1/2, pages 15-25.
32. [Setiono00b] Setiono, R. Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence in Medicine, 2000, Vol 18, No. 3, pages 205-219.
33. [Setiono95] Setiono, R. & Liu, H. Understanding neural networks via rule extraction. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Canada, August 1995, pages 480-485.
34. [Setiono99] Setiono, R. & Leow, W. K. On mapping decision trees and neural networks. Knowledge-Based Systems, 12(1999) p.95-99. Elsevier Press, Netherlands. 1999.
35. [Setiono99a] Setiono, R. & Liu, H. A connectionist approach to generating oblique decision trees. IEEE Transactions on Systems, Man, and Cybernetics, 1999, Vol. 29, No. 3, pages 440-444.
36. [Sima00] Sima, J. & Cervenka, J. Neural Knowledge Processing in Expert Systems. 13th chapter in Knowledge-Based Neurocomputing, eds. I. Cloete, J. M. Zurada, Cambridge: The MIT Press, Feb 2000, 419-466.
37. [Sima00a] Sima, J. Review of integration strategies in neural hybrid systems. Euro-International Symposium on Computational Intelligence (E-ISCI'2000), Kosice, Slovakia, In Book Quo Vadis Computational Intelligence? New Trends and Approaches in Computational Intelligence, eds. P. Sincak, V. Vascak, 355-360, Berlin: Springer-Verlag, Studies in Fuzziness and Soft Computing, Vol. 54, 2000.
38. [Sun91] Sun, R. Integrating Rules and Connectionism for Robust Reasoning. A connectinist Architecture with Dual Representation. Ph.D. Thesis, Brandeis University. Waltham, MA. T.Rep. CS-91-160. 1991.
39. [Tickle98] Tickle, A. B., Andrews, R., Golea, M., & Diederich J. The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transactions on Neural Networks, 9(6):1057, November 1998.
40. [Touretzki88] Touretzki, D. S. & Hinton, G. E. A Distributed Connectionist Production System. Cognitive Science, 12:423-466. 1988.
41. [Touretzki90] Touretzki, D. S. Boltzcons: Dynamic Symbol Structures in a Connectionist Network. Artificial Intelligence, 46(1-2).
42. [Towell91] Towell, G. Symbolic Knowledge and Neural Networks: Insertion, Refinement and Extraction. Ph.D. Thesis, University of Wisconsin-Madison - Computer. Science Dept. 1991. (or ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/ towell.thesis.*.ps)
43. [Towell93] Towell, G. & Shavlik, J. Extracting Refined Rules From Knowledge-Based Neural Nets. In: Machine Learning. pp.71-101, 13. (Kluwer Academic Publishers - Boston, 1993. (or ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/towell.mlj93.ps)
44. [Towell94] Towell, G. & Shavlik, J. Knowledge-Based Neural Nets. In: Artificial Intelligence. pp.119-165, 70. (1994). (or ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/towell.aij94.ps)
