Abstract
An overview of case-based learning techniques for classification problem solving from the research areas of expert systems, statistics, and neuronal nets is presented, together with some results from comparative evalutions. We broadly define a problem solver to be able to “learn from cases” if it usually performs better with every new case.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aghassi, D. (1990): Evaluating Case-Based Reasoning for Heart Failure Diagnosis, Master Thesis, Massachusetts Institute of Technology, Computer Science Lab., MIT/LCS/TR-478
Bamberger, S., Gappa, U., Goos, K. und Poeck, K. (1993): Teilautomatische Wissenstransformationen zur Unterstützung der Wissensakquisition. In: Proc. Expertensy-steme-93, Springer, Informatik Aktuell, 153–166
Benjamins, V. (1993): Problem Solving Methods for Diagnosis, PhD thesis, University of Amsterdam, Amsterdam, The Netherlands
Blum, R. (1988): Modeling and Encoding Clinical Causal Relations in a Medical Knowledge Base, in: Miller, P. (ed.): Selected Topics in Medical Artificial Intelligence, Springer
Clancey, W. (1985): Heuristic Classification, AI-Journal20, 215–251
Davis, R. (1984): Diagnostic Reasoning Based on Structure and Function, AI Journal 24, 347–411
De Jong, G. and Money, R. (1986): Explanation-Based Learning: An Alternative View, Machine Learning 1, 145–176
De Kleer, J. and Williams, B. (1987): Diagnosing Multiple Faults, AI Journal 32, 97–130
De Kleer, J. and Williams, B. (1989): Diagnosis with Behavioral Models, in: IJCAI-89, 1324–1330
Fisher, R. (1936): The Use of Multiple Measurements in Taxonomie Problems, Annals of Eugenics 7, 179–188
Hart, P. (1967): The Condensed Nearest Neighbor Rule, Transactions on Information Theory 14, 515–516
Heckerman, D. (1991): Probabilistic Similarity Networks, MIT Press
Hinton, G. (1989): Connectionist Learning Procedures, Ai-Journal40, Special issue on Machine Learning, 185–234
Huber, K.-P. and Nakhaeizadeh, G. (1993): Maschinelle Lernverfahren zur Unterstützung beim Wissenserwerb von Diagnose-Expertensystemen, in: Proc. Expertensysteme-93, Springer, Informatik-Aktuell, 167–180
James, M. (1985): Classification Algorithms, Wiley
Koton, P. (1989): Using Experience in Learning and Problem Solving, PhD-Thesis, Massachusetts Institute of Technology, Computer Science Lab., MIT/LCS/TR-441
Kodratoff, Y. and Michalski, R. (eds.) (1990): Machine Learning Vol III, Morgan Kaufmann
Kolodner, J. (1993): Case-Based Reasoning, Morgan Kaufmann
Lebowitz, M. (1990): The Utility of Similarity-Based Learning in a World Needing Explanation, in: Kodratoff, Y. and Michalski, R. (eds.): Machine Learning Vol. III, Chap. 15, Morgan Kaufmann
Lusted, L. (1968): Introduction to Medical Decision Making, Thomas Books
Michalski, R. (1983): A Theory and Methodology of Inductive Learning, AI-Journal20, 111–161
Minton et al. (1989): Explanation-Based Learning: A Problems Solving Perspective, AI-Journal40, 63–118
Michie, D., Spiegelhalter D., and Taylor, C. (1994): Book about the Esprit-Project Statlog, Simon & Schuster, Campus 400, Maylands Avenue, Hemel Hepstead Herts HP2 7EZ, UK, to appear around February 1994
Mitchell, T. (1977): Version Spaces: A Candidate Elimination Approach to Rule Learning, IJCAI-77, 305–310
O’Neil, T. (1992): Error Rates of non-Bayes Classification Rules and the Robustness of Fishers Linear Discriminant Function, Biometrika 79, 177–184
Pearl, J. (1988): Probabilistic Reasoning in Intelligent Systems, Morgan Kaufman
Peng, Y. and Reggia, J. (1990): Abductive Inference Models for Diagnostic Problem-Solving, Springer
Prem, E., Mackinger, M., and Dorffner, G. Porenta, G. and Sochor, H. (1993): Concept Support as a Method for Programming Neural Networks with Symbolic Knowledge, in: Proc. GWAI-92, Lecture Notes in Artificial Intelligence, Springer-Verlag, 166–175
Puppe, F., and Goos, K. (1991): Improving Case-Based Classification with Expert Knowledge, in: Proc. GWAI-91, Springer, Informatik-Fachberichte 285, 196–205
Puppe, F. (1993): Systemtatic Introduction to Expert Systems, Springer
Quinlan, J. (1986): Induction of Decision Trees, Machine Learning 1, 81–106
Quinlan, J. (1987): Simplifying Decision Trees, International Journal of Man-Machine-Studies 27, 221–234
Quinlan, J. (1992): C4.5: Prograpmms for Machine Learning, Morgan Kaufmann
Rausseck, M. (1992): Diagnostik mit selbstorganisierenden Karten am Beispiel der Motordiagnose, Diplomarbeit, Universität Karlsruhe, Institut für Logik, Komplexität und Deduktionssysteme
Reggia, J. and Peng, Y. (1990): Abductive Inference Models for Diagnostic Problem-Solving, Springer
Ritter, H. Martinez, T. and Schulten, K. (1991): Neuronale Netze, Addison Wesley
Weiss, S. and Kulikowski, C. (1991): Computer Systems that Learn, Morgan Kaufmann
Wess, S. (1993): Patdex — ein Ansatz zur wissensbasierten und inkrementellen Verbesserung von Ähnlichkeitsbewertungen in der fallbasierten Diagnostik, in: Proc. Experten-systeme-93, Springer, Informatik Aktuell, 42–55
Winston, P. (1992): Artificial Intelligence, Addison Wesley, 3rd edition
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Puppe, F. (1994). Learning from Cases for Classification Problem Solving. In: Bock, HH., Lenski, W., Richter, M.M. (eds) Information Systems and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-46808-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-46808-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58057-7
Online ISBN: 978-3-642-46808-7
eBook Packages: Springer Book Archive