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Publications of the UW ML Research Group
This page contains relevant information about publications by the members of the Machine Learning Research Group (MLRG) at the University of Wisconsin - Madison.
Journal Articles
Conference Papers
Book Chapters
Workshop Papers
Tech Reports
PhD Theses
Bibtex file
- R. Maclin & J. W. Shavlik (1996).
Creating Advice-Taking Reinforcement Learners.
Machine Learning, 22, pp. 251-281.
Abstract.
- D. W. Opitz & J. W. Shavlik (1995).
Dynamically Adding Symbolically Meaningful Nodes to Knowledge-Based Neural Networks.
Knowledge-Based Systems, 8, pp. 301-311.
Abstract.
- K. J. Cherkauer (1995).
Stuffing Mind into Computer: Knowledge and Learning for Intelligent Systems.
Informatica, 19, pp. 501-511.
Abstract.
- J. W. Shavlik (1994).
A Framework for Combining Symbolic and Neural Learning.
Machine Learning, 14, pp. 321-331.
(The on-line file is an extended version of the journal article.)
Abstract.
- G. G. Towell & J. W. Shavlik (1994).
Knowledge-Based Artificial Neural Networks.
Artificial Intelligence, 70, pp. 119-165.
Abstract.
- R. Maclin & J. W. Shavlik (1993).
Using Knowledge-based Neural Networks To Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding.
Machine Learning, 11, pp. 195-215.
Abstract.
- G. G. Towell & J. W. Shavlik (1993).
The Extraction of Refined Rules from Knowledge-Based Neural Networks.
Machine Learning, 13, pp. 71-101.
Abstract.
- M. W. Craven & J. W. Shavlik (1993).
Machine Learning Approaches to Gene Recognition.
IEEE Expert, 9.
(The on-line file is a variant of the journal article.)
Abstract.
- M. W. Craven & J. W. Shavlik (1992).
Visualizing Learning and Computation in Artificial Neural Networks.
International Journal on Artificial Intelligence Tools, 1, pp. 399-425.
Abstract.
- D. W. Opitz & J. W. Shavlik (1996).
Generating Accurate and Diverse Members of a Neural-Network Ensemble.
Advances in Neural Information Processing Systems, Denver, CO. MIT Press.
Abstract.
- K. J. Cherkauer & J. W. Shavlik (1996).
Rapid Quality Estimation of Neural Network Input Representations.
Advances in Neural Information Processing Systems, pp. 45-51, Denver, CO. MIT Press.
Abstract.
- M. W. Craven & J. W. Shavlik (1996).
Extracting Tree-Structured Representations of Trained Networks.
Advances in Neural Information Processing Systems, Denver, CO. MIT Press.
Abstract.
- C.F. Allex, S.F. Baldwin, J.W. Shavlik, & F.R. Blattner (1996).
Improving the Quality of Automatic DNA Sequence Assembly using Fluorescent Trace-Data Classifications.
Proceedings, Fourth International Conference on Intelligent Systems for Molecular Biology, pp. 3-14, St. Louis, MO. AAAI Press.
Abstract.
- K.J. Cherkauer & J.W. Shavlik (1996).
Growing Simpler Decision Trees to Facilitate Knowledge Discovery.
Proceedings, Second International Conference on Knowledge Discovery and Data Mining, pp. 315-318, Portland, OR. AAAI Press.
Abstract.
- J. C. Jackson & M. W. Craven (1996).
Learning Sparse Perceptrons.
Advances in Neural Information Processing Systems, Denver, CO. MIT Press.
Abstract.
- J. W. Shavlik (1996).
An Overview of Research at Wisconsin on Knowledge-Based Neural Networks.
Proceedings of the International Conference on Neural Networks, pp. 65-69, Washington, DC.
Abstract.
- R. Maclin & J. W. Shavlik (1995).
Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks.
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 524-530, Montreal, Canada.
Abstract.
- M. W. Craven, R. J. Mural, L. J. Hauser & E. C. Uberbacher (1995).
Predicting Protein Folding Classes without Overly Relying on Homology.
Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology, pp. 98-106, Cambridge, England. AAAI Press.
- M. W. Craven & J. W. Shavlik (1994).
Using Sampling and Queries to Extract Rules from Trained Neural Networks.
Proceedings of the Eleventh International Conference on Machine Learning, pp. 37-45, New Brunswick, NJ.
Abstract.
- R. Maclin & J. W. Shavlik (1994).
Incorporating Advice into Agents that Learn from Reinforcements.
Proceedings of the Twelfth National Conference on Artificial Intelligence, pp. 694-699, Seattle, WA.
Abstract.
- D. W. Opitz & J. W. Shavlik (1994).
Using Genetic Search to Refine Knowledge-Based Neural Networks.
Machine Learning: Proceedings of the Eleventh International Conference, pp. 208-216, New Brunswick, NJ. Morgan Kaufmann.
Abstract.
- M. W. Craven & J. W. Shavlik (1993).
Learning to Predict Reading Frames in E . coli DNA Sequences.
Proceedings of the 26th Hawaii International Conference on System Sciences, pp. 773-782, Wailea, HI. IEEE Computer Society Press.
Abstract.
- M. W. Craven & J. W. Shavlik (1993).
Learning to Represent Codons: A Challenge Problem for Constructive Induction.
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1319-1324, Chambery, France.
Abstract.
- M. W. Craven & J. W. Shavlik (1993).
Learning Symbolic Rules Using Artificial Neural Networks.
Proceedings of the Tenth International Conference on Machine Learning, pp. 73-80, Amherst, MA. Morgan Kaufmann.
Abstract.
- K. J. Cherkauer & J. W. Shavlik (1993).
Protein Structure Prediction: Selecting Salient Features From Large Candidate Pools.
Proceedings of the First International Conference on Intelligent Systems for Molecular Biology, pp. 74-82, Bethesda, MD. AAAI Press.
Abstract.
- D. W. Opitz & J. W. Shavlik (1993).
Heuristically Expanding Knowledge-Based Neural Networks.
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1360-1365, Chambery, France.
Abstract.
- G. G. Towell & J. W.Shavlik (1992).
Using Symbolic Learning to Improve Knowledge-Based Neural Networks.
Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 177-182, San Jose, CA.
Abstract.
- G. M. Scott, J. W. Shavlik & W. H. Ray (1992).
Refining PID Controllers using Neural Networks.
Advances in Neural Information Processing Systems, pp. 555-562, Denver, CO. Morgan Kaufmann.
Abstract.
- G. G. Towell & J. W. Shavlik (1992).
Interpretation of Artificial Neural Networks: Mapping knowledge-based Neural Networks into Rules.
Advances in Neural Information Processing Systems, pp. 977-984, Denver, CO. Morgan Kaufmann.
Abstract.
- R. Maclin & J. W. Shavlik (1992).
Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding.
Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 165-170, San Jose, CA.
Abstract.
- M. O. Noordewier, G. G. Towell & J. W. Shavlik (1991).
Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences.
Advances in Neural Information Processing Systems, pp. 530-536, Denver, CO. Morgan Kaufmann.
Abstract.
- G. G. Towell, J. W. Shavlik & M. O. Noordewier (1990).
Refinement of Approximate Domain Theories by Knowledge-Based Neural Networks.
Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 861-866, Boston, MA.
Abstract.
- D. W. Opitz & J. W. Shavlik (1995).
Using Heuristic Search to Expand Knowledge-Based Neural Networks.
In T. Petsche, S. Hanson & J. Shavlik, editors, Computational Learning Theory and Natural Learning Systems, Volume III. MIT Press.
- M. W. Craven & J. W. Shavlik (1995).
Investigating the Value of a Good Input Representation.
In T. Petsche, S. Hanson & J. Shavlik, editors, Computational Learning Theory and Natural Learning Systems, Volume III. MIT Press.
- R. Maclin & J. W. Shavlik (1994).
Refining Algorithms with Knowledge-Based Neural Networks: Improving the Chou-Fasman Algorithm for Protein Folding.
In S. Hanson, G. Drastal & R. Rivest, editors, Computational Learning Theory and Natural Learning Systems, Volume I. MIT Press.
- K. J. Cherkauer & J. W. Shavlik (1994).
Selecting Salient Features for Machine Learning from Large Candidate Pools through Parallel Decision-Tree Construction.
In H. Kitano, editor, Massively Parallel Artificial Intelligence. AAAI Press/The MIT Press, Menlo Park, CA.
Abstract.
- G. G. Towell & J. W. Shavlik (1993).
Refining Symbolic Knowledge Using Neural Networks.
In R. S. Michalski & G. Tecuci, editors, Machine Learning: An Integrated Approach. Morgan Kaufmann, San Mateo, CA.
- K.J. Cherkauer (1996).
Human Expert-Level Performance on a Scientific Image Analysis Task by a System Using Combined Artificial Neural Networks.
Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms Wkshp, 13th Nat Conf on Artificial Intelligence, pp. 15-21, Portland, OR.
Abstract.
- K. J. Cherkauer & J. W. Shavlik (1995).
Rapidly Estimating the Quality of Input Representations for Neural Networks.
Working Notes of the IJCAI-95 Workshop on Data Engineering for Inductive Learning, Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada.
Abstract.
- M. W. Craven & J. W. Shavlik (1995).
Extracting Comprehensible Concept Representations from Trained Neural Networks.
Presented at the IJCAI Workshop on Comprehensibility in Machine Learning, Montreal, Quebec, Canada.
Abstract.
- D. W. Opitz & J. W. Shavlik (1994).
Genetically Refining Topologies of Knowledge-Based Neural Networks.
International Symposium on Integrating Knowledge and Neural Heuristics, pp. 57-66, Pensacola, FL.
Abstract.
- J. W. Shavlik (1991).
Finding Genes by Case-Based Reasoning in the Presence of Noisy Case Boundaries.
Proceedings of the DARPA Cased-Based Reasoning Workshop, pp. 327-338.
Abstract.
- G. G. Towell, M. W. Craven & J. W. Shavlik (1991).
Constructive Induction in Knowledge-Based Neural Networks.
Proceedings of the Eighth International Machine Learning Workshop, pp. 213-217, Evanston, IL.
Abstract.
- R. Maclin & J. W. Shavlik (1991).
Refining Domain Theories Expressed as Finite-State Automata.
Proceedings of the Eighth International Machine Learning Workshop, pp. 524-528, Evanston, IL.
Abstract.
- M. W. Craven (1996).
Extracting Comprehensible Models from Trained Neural Networks.
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-96-1326)
Abstract.
- D. W. Opitz (1995).
An Anytime Approach to Connectionist Theory Refinement: Refining the Topologies of Knowledge-Based Neural Networks.
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-95-1281)
Abstract.
- R. Maclin (1995).
Learning from Instruction and Experience: Methods for Incorporating Procedural Domain Theories into Knowledge-Based Neural Networks.
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-95-1285)
Abstract.
(This publication is contained in several postscript files.)
part 1
part 2
- Eric Gutstein (1993).
SIFT: A Self-Improving Fractions Tutor.
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
- G. G. Towell (1991).
Symbolic Knowledge and Neural Networks: Insertion, Refinement and Extraction.
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report 1072 [out of print].)
Abstract.
(This publication is contained in several postscript files.)
part 1
part 2
part 3
part 4
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