Summary
During the last few years research in machine learning (ML) has grown explosively. Today, ML-research is an essential component of the research program at many top universities and R&D centers. This research is often truly interdisciplinary in character, bringing together researchers working on such diverse topics as computer science, neural nets, artificial intelligence, theory of computation, computer architecture, speech and pattern recognition, and neurobiology.
The goals of this research are ambitious, inasmuch as they include building machines which respond adaptively and intelligently to changes in their environment without being reprogrammed by humans.
This article surveys the state of the art in machine learning, and briefly describes the efforts undertaken by Siemens and the Massachusetts Institute of Technology in this area. As the field is large and tremendously active, a number of references are included for the reader who wishes to explore further.
Our ultimate objective is to make programs that learn from their experience as effectively as humans do. (John McCarthy)
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Literature
D. Angluin, C. H. Smith. Inductive inference: theory and methods. Computing Surveys, 15(3): 237–269, Sept. 1983.
J. G. Carbonell, P. Langley. Machine Learning. In S. C. Shapiro (ed). Encyclopedia of AI, Vol 1: 464–488, John Wiley and Sons, 1987.
M. A. Fischler, O. Firschein. Intelligence; The Eye, the Brain and the Computer. Addison Wesley Publishing Company, 1987.
J. J. Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proc. Natl. Acad. Sci. USA 79, 2554–2558, 1982.
J. S. Judd. Complexity of Connectionist Learning with Various Node Functions. Technical Report of Computer and Information Science, University of Massachusetts at Amherst, July 1987.
M. Kearns, M. Li, L. Pitt, L. Valiant. Recent results on boolean concept learning. In Proceedings of the Fourth International Workshop on Machine Learning, University of California at Irvine, June 1987.
J. McCarthy. Programs with common sense. In Proceedings of the Symposium on the Mechanization of Thought Processes. National Physical Laboratory, 1958.
R. S. Michalsky, J. G. Carbonell, T. M. Mitchell (eds). Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann, 1983.
M. Minsky, S. Papert. Perceptrons: An Introduction to Computations Geometry. The MIT Press, 1969.
T. M. Mitchell. Version spaces: a candidate elimination approach to rule learning. In Proceedings of the Fifth International Joint Conference on AI, Cambridge, Mass., Aug. 1977.
F. Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65:386–407, 1958.
D. E. Rumelhart, G. E. Hinton, R. J. Williams. Learning Internal Representations by Error Propagation. University of California at San Diego, ICS Report 8506, Sept. 1985.
A. L. Samuel. Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3:211-229, July 1959. (Reprinted in Computers and Thought, (eds. E. A. Feigenbaum and J. Feldman), McGraw-Hill, 1963).
T. J. Sejnowski, C. R. Rosenberg. Parallel networks that learn to pronounce English text. Journal of Complex Systems, 1(1):145–168, Feb. 1987.
A. M. Turing. Computing machinery and intelligence. Mind, 59:433-460, Oct. 1950 (Reprinted in Computers and Thought, (eds. E. A. Feigenbaum and J. Feldman), McGraw-Hill, 1963).
L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11): 1134–1142, Nov. 1984.
P. H. Winston. Learning structural descriptions from examples. In P. H. Winston (ed), The Psychology of Computer Vision, McGraw-Hill, 1975.
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© 1991 Springer-Verlag Berlin Heidelberg
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Rivest, R.L., Remmele, W. (1991). Machine Learning. In: Schwärtzel, H. (eds) Angewandte Informatik und Software / Applied Computer Science and Software. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-93501-5_16
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DOI: https://doi.org/10.1007/978-3-642-93501-5_16
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