Summary
The Nearest-Neighbor Classification has a long tradition in the area of pattern recognition while knowledge-based systems apply mainly symbolic learning algorithms. There is a strong relationship between Nearest-Neighbor Classification and learning. The increasing number of cases and the adaptation of the similarity measure are used to improve the classification ability. Nowadays, Nearest-Neighbor Classification is applied in knowledge-based systems by a technique called case-based reasoning. In this paper we present first results from a comparison of case-based and symbolic learning systems.
The presented work was partly supported by the Deutsche Forschungsgemeinschaft, SFB 314: “Artificial Intelligence and Knowledge Based Systems” and the Project IND-CBL.
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.
Similar content being viewed by others
References
AHA, D.W. (1991): Case-Based Learning Algorithms. In: Bareiss, Ray (ed), Proceedings: Case-Based Reasoning Workshop. San Mateo, California: Morgan Kaufmann Publishers, for Darpa. Washington, D.C., USA, May 8–10, 1991.
AHA, D.W., KIBLER, D., and ALBERT, M.K. (1991): Instance-Based Learning Algorithms. Machine Learning, 6, 37–66. March 1991.
AHA, D.W., and KIBLER, D. (1989): Noise-Tolerant Instance-Based Learning Algorithms. Pages 794–799 of: Proceedings of the 11th International Conference on Artificial Intelligence IJCAI-89. IJCAI. Detroit, Michigan, USA.
COST, S., and SALZBERG, S. (1993): A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning, 10(1), 56–78.
DASARATHY, B. (1990): Nearest Neighbor Norms: NN Pattern Classification Techniques. IEEE Computer Society Press.
GLOBIG, C. (1993): Fallbasiertes und Symbolisches Lernen. Diplomarbeit, Universität Kaiserslautern.
JANTKE, K.P. (1992): Case-Based Learning in Inductive Inference. In: Proc. COLT-92.
KOLODNER, J.L. (1991): Improving Human Decision Making through Case-Based Decision Aiding. AI Magazine, 91(2), 52–68.
MICHALSKI, R., CARBONELL, J.G., and MITCHELL, T. (eds). (1983): Machine Learning: An Artificial Intelligence Approach. Vol. 1. Palo Alto, California: Tioga.
MITCHELL, T.M. (1982): Gerneralization as Search. Artificial Intelligence, 18(2), 203–226.
RICHTER, M.M. (1992): Classification and Learning of Similarity Measures. In: Proc. of the 16th Annual Conference of the German Society for Classification (Gesellschaft für Klassifikation e. V.). Springer Verlag.
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
Globig, C., Wess, S. (1994). Symbolic Learning and Nearest-Neighbor Classification. 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_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-46808-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58057-7
Online ISBN: 978-3-642-46808-7
eBook Packages: Springer Book Archive