Synonyms
Definition
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).
Background
Bruner et al. (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 and to respond to...
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
Recommended Reading
Banerji RB (1960) An information processing program for object recognition. Gen Syst 5:117–127
Banerji RB (1962) The description list of concepts. Commun Assoc Comput Mach 5(8):426–432
Banerji RB (1964) A language for the description of concepts. Gen Syst 9:135–141
Banerji RB (1980) Artificial intelligence: a theoretical approach. North Holland, New York
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont
Bruner JS, Goodnow JJ, Austin GA (1956) A study of thinking. Wiley, New York
Cohen WW (1995) In fast effective rule induction. In: Proceedings of the twelfth international conference on machine learning, Lake Tahoe. Morgan Kaufmann, Menlo Park
Cohen BL, Sammut CA (1982) Object recognition and concept learning with CONFUCIUS. Pattern Recognit J 15(4):309–316
Hayes-Roth F (1973) A structural approach to pattern learning and the acquisition of classificatory power. In: First international joint conference on pattern recognition, Washington, DC, pp 343–355
Hayes-Roth F, McDermott J (1977) Knowledge acquisition from structural descriptions. In: Fifth international joint conference on artificial intelligence, Cambridge, MA, pp 356–362
Hunt EB, Marin J, Stone PJ (1966) Experiments in induction. Academic, New York
Michalski RS (1973) Discovering classification rules using variable valued logic system VL1. In: Third international joint conference on artificial intelligence, Stanford, pp 162–172
Michalski RS (1983) A theory and methodology of inductive learning. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning: an artificial intelligence approach. Tioga, Palo Alto
Mitchell TM (1997) Machine learning. McGraw-Hill, New York
Pennypacker JC (1963) An elementary information processor for object recognition. SRC No. 30-I-63-1. Case Institute of Technology
Quinlan JR (1983) Learning efficient classification procedures and their application to chess end games. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning: an artificial intelligence approach. Tioga, Palo Alto
Quinlan JR (1986) The effect of noise on concept learning. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning: an artificial intelligence approach, vol 2. Morgan Kaufmann, Los Altos
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
Sammut CA, Banerji RB (1986) Learning concepts by asking questions. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning: an artificial intelligence approach, vol 2. Morgan-Kaufmann, Los Altos, pp 167–192
Vere S (1975) Induction of concepts in the predicate calculus. In: Fourth international joint conference on artificial intelligence, Tbilisi, pp 351–356
Vere SA (1977) Induction of relational productions in the presence of background information. In: Fifth international joint conference on artificial intelligence, Cambridge, MA
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
Sammut, C. (2017). Concept Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_154
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_154
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering