This page contains relevant information about publications by the members of the Machine Learning Research Group (MLRG) at the University of Wisconsin - Madison.
J. C. Jackson & M. W. Craven (1996). Learning Sparse Perceptrons.
Advances in Neural Information Processing Systems, pp. 654-662, Denver, CO. MIT Press.
Abstract.
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.
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.
R. J. Mooney, P. Melville, L. P. Rupert Tang, J. Shavlik, I. de Castro Dutra, D. Page, V. Santos Costa (2002). Relational Data Mining with Inductive Logic Programming for Link Discovery.
Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, Maryland, USA.
A longer and updated version of this paper appears as a chapter in ``Data Mining: Next Generation Challenges and Future Directions'', H. Kargupta and A. Joshi (eds.), by AAAI/MIT Press
Abstract.
G. M. Fung, O. L. Mangasarian, & J. W. Shavlik (2001). Knowledge-based Support Vector Machine Classifiers.
Data Mining Institute, University of Wisconsin, DMI TR 01-09.
(A shorter version of this paper appears in Advances in Neural Information Processing [NIPS], 2002)
Abstract.
D. W. Opitz & J. W. Shavlik (1995). Generating Accurate and Diverse Members of a Neural-Network Ensemble.
Department of Computer Sciences, University of Wisconsin, Machine Learning Research Group Working Paper 95-2.
(A version of this paper appears in Advances in Neural Information Processing, vol. 8, 1996)
Abstract.
B. Geisler (2002).
An Empirical Study of Machine Learning Algorithms Applied to Modeling Player Behavior in a 'First Person Shooter' Video Game.
M.S. thesis, Department of Computer Sciences, University of Wisconsin-Madison.
Abstract.
This publication is available in PDF and available in postscript.
T. Eliassi-Rad (2001).
Building Intelligent Agents that Learn to Retrieve and Extract Information.
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-01-1431)
Abstract.
This publication is available in PDF and available in postscript.
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 [out of print])
Abstract.
This publication is available in PDF and available in postscript.
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 [out of print])
Abstract.
This publication is contained in the following 2 postscript files File 1, File 2 and contained in the following 2 PDF files File 1, File 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 the following 4 postscript files File 1, File 2, File 3, File 4.
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