Neural-Symbolic Learning
Neural networks and symbolic learning techniques can be seen as operating at different levels of abstraction. Our work focuses on understanding differences between their capabilities, and on combining their strengths.
Leif Johnson Ph.D. Student leif [at] cs utexas edu
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Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases 1996
J. Jeffrey Mahoney, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 113 pages.
Revising Bayesian Network Parameters Using Backpropagation 1996
Sowmya Ramachandran and Raymond J. Mooney, In Proceedings of the International Conference on Neural Networks (ICNN-96), Special Session on Knowledge-Based Artificial Neural Networks, pp. 82--87, Washington DC, June 1996.
Refinement of Bayesian Networks by Combining Connectionist and Symbolic Techniques 1995
Sowmya Ramachandran, . Unpublished Ph.D. Thesis Proposal.
Comparing Methods For Refining Certainty Factor Rule-Bases 1994
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 173--180, Rutgers, NJ, July 1994.
Modifying Network Architectures For Certainty-Factor Rule-Base Revision 1994
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the International Symposium on Integrating Knowledge and Neural Heuristics (ISIKNH-94), pp. 75--85, Pensacola, FL, May 1994.
Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases 1993
J. Jeffrey Mahoney and Raymond J. Mooney, Connection Science (1993), pp. 339-364.
Combining Symbolic and Neural Learning to Revise Probabilistic Theories 1992
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the ML92 Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.
Growing Layers of Perceptrons: Introducing the Extentron Algorithm 1992
Paul T. Baffes and John M. Zelle, In Proceedings of the 1992 International Joint Conference on Neural Networks, pp. 392--397, Baltimore, MD, June 1992.
Symbolic and Neural Learning Algorithms: An Experimental Comparison 1991
J.W. Shavlik, Raymond J. Mooney and G. Towell, Machine Learning, Vol. 6 (1991), pp. 111-143. Reprinted in {it Readings in Knowledge Acquisition and Learning}, Bruce G. Buchanan and David C. Wilkins (eds.), Morgan Kaufman, San Mateo, CA, 19...
An Experimental Comparison of Symbolic and Connectionist Learning Algorithms 1989
Raymond J. Mooney, J.W. Shavlik, G. Towell and A. Gove, In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pp. 775-780, Detroit, MI, August 1989. Reprinted in ``Readings in Machine Learning'', Jude ...
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems 1989
Douglas Fisher, Kathleen McKusick, Raymond J. Mooney, Jude W. Shavlik, and Geoffrey Towell, In Proceedings of the Sixth International Workshop on Machine Learning, pp. 169--173, Ithaca, New York 1989.