The term concept learning is originated in psychology, where it refers to the human ability to learn categories for object and to recognize new instances of those categories. In machine learning, concept is more formally defined as “inferring a boolean-valued function from training examples of its inputs and outputs” (Mitchell, 1997).
Bruner, Goodnow, and Austin (1956) published their book A Study of Thinking, which became a landmark in psychology and would later have a major impact on machine learning. The experiments reported by Bruner, Goodnow, and Austin were directed toward understanding a human’s ability to categorize and how categories are learned.
We begin with what seems a paradox. The world of experience of any normal man is composed of a tremendous array of discriminably different objects, events, people, impressions…But were we to utilize fully our capacity for registering the differences in things...
- Banerji, R. B. (1960). An information processing program for object recognition. General Systems, 5, 117–127.Google Scholar
- Banerji, R. B. (1964). A Language for the Description of Concepts. General Systems, 9, 135–141.Google Scholar
- Banerji, R. B. (1980). Artificial intelligence: A theoretical approach. New York: North Holland.Google Scholar
- Bruner, J. S., Goodnow, J. J., & Austin, G. A. (1956). A study of thinking. New York: Wiley.Google Scholar
- Cohen, B. L., & Sammut, C. A. (1982). Object recognition and concept learning with CONFUCIUS. Pattern Recognition Journal, 15(4), 309–316.Google Scholar
- Cohen, W. W. (1995). In fast effective rule induction. In Proceedings of the twelfth international conference on machine learning, Lake Tahoe, California. Menlo Park: Morgan Kaufmann.Google Scholar
- Hayes-Roth, F. (1973). A structural approach to pattern learning and the acquisition of classificatory power. In First international joint conference on pattern recognition (pp. 343–355). Washington, D.C.Google Scholar
- Hayes-Roth, F., & McDermott, J. (1977). Knowledge acquisition from structural descriptions. In Fifth international joint conference on artificial intelligence (pp. 356–362). Cambridge, MA.Google Scholar
- Hunt, E. B., Marin, J., & Stone, P. J. (1966). Experiments in induction. New York: Academic.Google Scholar
- Michalski, R. S. (1973). Discovering classification rules using variable valued logic system VL1. In Third international joint conference on artificial intelligence (pp. 162–172). Stanford, CA.Google Scholar
- Michalski, R. S. (1983). A theory and methodology of inductive learning. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. Palo Alto: Tioga.Google Scholar
- Pennypacker, J. C. (1963). An elementary information processor for object recognition. SRC No. 30-I-63-1. Case Institute of Technology.Google Scholar
- Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess end games. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. Palo Alto: Tioga.Google Scholar
- Quinlan, J. R. (1986). The effect of noise on concept learning. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 2). Los Altos: Morgan Kaufmann.Google Scholar
- Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.Google Scholar
- Sammut, C. A., & Banerji, R. B. (1986). Learning concepts by asking questions. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 2, pp. 167–192). Los Altos, CA: Morgan-Kaufmann.Google Scholar
- Vere, S. (1975). Induction of concepts in the predicate calculus. In Fourth international joint conference on artificial intelligence (pp. 351–356). Tbilisi, Georgia, USSR.Google Scholar
- Vere, S. A. (1977). Induction of relational productions in the presence of background information. In Fifth international joint conference on artificial intelligence. Cambridge, MA.Google Scholar
- Winston, P. H. (1970). Learning structural descriptions from examples. Unpublished PhD Thesis, MIT Artificial Intelligence Laboratory.Google Scholar