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M - Machine learning to Multicast

Published online by Cambridge University Press:  17 May 2010

Robert Plant
Affiliation:
University of Miami
Stephen Murrell
Affiliation:
University of Miami
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Summary

Foundation concept: Artificial intelligence.

Definition: Machine learning is the ability of programs to make inferences and expand their understanding of the domain in which they operate.

Overview

The term Machine learning is usually associated with automated learning or Self-programming capabilities rather than the production of solutions from static knowledge bases. Mechanisms for learning under current research include learning by knowledge acquisition, learning by examples, neural networks, case-based reasoning (CBR), genetic algorithms, and learning by discovery.

Knowledge acquisition is a vital component of machine learning and is the mechanism through which a system gathers knowledge, which then has to be represented internally to provide the basis for inference. Major challenges to researchers into machine learning lie in each part of this process: how to capture knowledge of different types (from pictures, speech, text, diagrams), how to represent each of these knowledge types, how to integrate them all, and, of course, how to infer useful new knowledge from old. In light of the multitude of different types of knowledge that systems need to acquire and be able to infer from, many techniques have been developed to assist in this process, including computational learning theory, explanation-based learning, delayed reinforcement learning, temporal difference learning, and using version spaces for learning.

One of the key problems still facing researchers is that of common sense reasoning; whereas a child having once touched a hot-plate can extrapolate that anything glowing red is potentially dangerous, this ability to generalize an experience is very difficult for programmers to build into computer systems, because every generalization is context dependent and thus variable in nature.

Type
Chapter
Information
An Executive's Guide to Information Technology
Principles, Business Models, and Terminology
, pp. 207 - 220
Publisher: Cambridge University Press
Print publication year: 2007

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References

Mitchell, T. (1997). Machine Learning (New York, McGraw-Hill).Google Scholar
C. Matuszek, M. Witbrock, R. Kahlert, J. Cabral, D. Schneider, P. Shah, and D. Lenat (2005). “Searching for common sense: populating Cyc from the Web,” in Proceedings, The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, Pittsburgh, Pennsylvania, ed. M. M. Veloso and S. Kambhampati (Cambridge, MA, AAAI Press/MIT Press), pp. 1430–1435.
Associated terminology: Neural networks, Knowledge-based systems.
The Journal of Software Maintenance: Research and Practice (New York, John Wiley and Sons).
Associated terminology: Cobol, ERP, Software metrics.
Bernstein, P. (1996). “Middleware: a model for distributed system services,”Communications of the A. C. M., Volume 39, No. 2.Google Scholar
Associated terminology: ERP.
N. Venkatraman (1997). “Beyond outsourcing: managing IT resources as a value center,” Sloan Management Review, Spring.
MIS Quarterly.
Communications of the A. C.M.
Information Systems Research.
Journal of MIS.
Information & Management.
European Journal of Information Systems.
Journal of Information Technology.
Associated terminology: Chief information officer, Enterprise resource planning.
Banks, M. (2000). The Modem Reference: The Complete Guide to PC Communications (New York, Cyberage Books).Google Scholar
Associated terminology: Bandwidth, Cable, and connectors, Voice over IP.

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