ACM Home Page
Please provide us with feedback. Feedback
Applications of machine learning and rule induction
Full text PdfPdf (554 KB)
Source
Communications of the ACM archive
Volume 38 ,  Issue 11  (November 1995) table of contents
Pages: 54 - 64  
Year of Publication: 1995
ISSN:0001-0782
Authors
Pat Langley  Stanford Univ., Stanford, CA
Herbert A. Simon  Carnegie Mellon Univ., Pittsburgh, PA
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 49,   Downloads (12 Months): 231,   Citation Count: 33
Additional Information:

abstract   references   cited by   index terms   review   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/219717.219768
What is a DOI?

ABSTRACT

Machine learning is the study of computational methods for improving performance by mechanizing the acquisition of knowledge from experience. Expert performance requires much domain-specific knowledge, and knowledge engineering has produced hundreds of AI expert systems that are now used regularly in industry. Machine learning aims to provide increasing levels of automation in the knowledge engineering process, replacing much time-consuming human activity with automatic techniques that improve accuracy or efficiency by discovering and exploiting regularities in training data. The ultimate test of machine learning is its ability to produce systems that are used regularly in industry, education, and elsewhere.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Biggs, D., de Ville, B., and Suen, E. A method of choosing multiway partitions for classification and decision trees. J. Applied Statistics 18, (1991), 49-62.
 
2
Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, CJ. Classification and Regression Trees. Wadsworth, Belmont, 1984.
 
3
 
4
E1 Attar, M. and Hamery, X. Industrial expert system acquired by machine learning. Applied Artificial Intelligence 8 (1994), 497-542.
 
5
 
6
Fayyad, U.M., Smyth, P., Weir, N., and Djorgovski, S. Automated analysis and exploration of image databases: Results, progress, and challenges. J. Intelligent Info. Syst. 4 (1995), 1-19.
 
7
 
8
Giordana, A., Neri, F., and Saitta, L. Automated learning for industrial diagnosis. In P. Langley and Y. Kodratoff, Eds., Fielded Applications of Machine Learning. Morgan Kaufmann, San Francisco, to be published.
 
9
Guilfoyle, C. Ten minutes to lay the foundations. Expert Systems User 8 (Aug. 1986), 16-19.
 
10
 
11
Karba, N., and Drole, R. Expert system for the cold rolling mill of the Steel Works Jesenice. In Proceedings of the Thirteenth Symposium on Information Technologies. (Sarajevo, 1990).
 
12
Kibler, D., and Langley, P. Machine learning as an experimental science. In Proceedings of the Third European Working Session on Learning. (Glasgow, Scotland, 1988), Pittman, pp. 81-92.
 
13
Langley, P., Drastal, G., Rao, B., and Greiner, R. Theory revision in fault hierarchies. In Proceedings of the Fifth International Workshop on Principals of Diagnosis. (New Paltz, NY 1994).
 
14
Leech, W.J. A rule-based process control method with feedback. Advances in Instrumentation 41 (1986), 169-175.
 
15
 
16
Michie, D. Problems of computer-aided concept formation. In J.R. Quinlan, Ed., Applications of Expert Systems, Vol. 2. Addison- Wesley, Wokingham, UK, 1989.
 
17
 
18
Modesitt, K.L. Inductive knowledge acquisition: A case study of Scotty. In K.L. McGraw and C.R. Westphal, Eds., Readings in Knowledge Acquisition: Current Practices and Trends. Ellis Hotwood, Chichester, UK, 1990.
 
19
Muggleton, S., King, R.D., and Sternberg, MJ.E. Protein secondary structure prediction using logic-based machine learning. Protein Engineering 5 (1992), 647-657.
 
20
 
21
 
22
Riese, C. Transformer fault detection and diagnosis using Rule- Master by Radian. Tech. Rep., Radian Corp., Austin, TX, 1984.
 
23
Samuelson, C., and Rayner, M. Quantitative evaluation of explanation-based learning as an optimization tool for a largescale natural language system. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. (Sydney, Australia, 1991), Morgan Kaufmann, pp. 609-615.
 
24
 
25
Zubrick, S. M. and Riese, C. E. An expert system to aid in severe thunderstorm forecasting. In Proceedings of the Fourteenth Conference on Severe Local Storms. (Indianapolis, Ind., 1985).

CITED BY  33


REVIEW

"Daniel L. Chester : Reviewer"

Rule induction, one of the five basic paradigms in machine learning, is covered most interestingly in this paper. (The other four paradigms are neural networks, case-based learning, genetic algorithms, and analytic learning.) Most   more...

Collaborative Colleagues:
Pat Langley: colleagues
Herbert A. Simon: colleagues