Abstract
Computer science has historically required programmers of systems to anticipate every possible behavior that could be desired and to program in advance all the knowledge and mechanisms needed to achieve it. Unfortunately, it has been found that such extensive and explicit programing is expensive and it still, in many cases, does not achieve the range of behaviors that might be needed. The only alternative is to have the machines program themselves to acquire the knowledge they need to function satisfactorily. This chapter has described many mechanisms for machine learning and provides an introduction to the field. Additional information can be found in the references and in the textbook on learning edited by Michalski et al. [83].
Supported in part by the U.S. Army Research Office under grant DAAG-29-84-K-0072
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© 1986 Springer-Verlag Berlin Heidelberg
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Biermann, A.W. (1986). Fundamental mechanisms in machine learning and inductive inference. In: Bibel, W., Jorrand, P. (eds) Fundamentals of Artificial Intelligence. Lecture Notes in Computer Science, vol 232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0022682
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DOI: https://doi.org/10.1007/BFb0022682
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