Trading MIPS and memory for knowledge engineering

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Trading MIPS and memory for knowledge engineering

Full text

Pdf (7.46 MB)

Source

Communications of the ACM archive
Volume 35 , Issue 8 (August 1992) table of contents

Pages: 48 - 64

Year of Publication: 1992

ISSN:0001-0782

Authors

Robert H. Creecy	U.S. Bureau of the Census, Washington, DC
Brij M. Masand	Thinking Machines Corp., Cambridge, MA
Stephen J. Smith	Thinking Machines Corp., Cambridge, MA
David L. Waltz	Thinking Machines Corp., Cambridge, MA

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 3, Downloads (12 Months): 27, Citation Count: 28

Additional Information:

references cited by index terms review collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/135226.135228 What is a DOI?

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	David W. Aha , Dennis Kibler , Marc K. Albert, Instance-Based Learning Algorithms, Machine Learning, v.6 n.1, p.37-66, Jan. 1991 [doi>10.1023/A:1022689900470]
	2	Anderberg, M.Cluster Analysis for Applicatins. Academic Press, New York, 1973.
	3	Appel, M. Automated industry and occupation coding. Development of Statistical Tools Seminar on Development of Statistical Expert Systems (DOSES), Luxembourg./, Dec- 1987.
	4	AppeI, M.V. and Hellerman, E. census Bureau expeiments with automated industry and occupation coding. In Proceedings' American Statistical Association, (1983), 32-40.
	5	Appet, M.V. and Scopp, T. Automdated industry and occupatin coding. Presented at Development of Statistical Expert Systems (DOSES), Luxembourg, Dec. 1987.
	6	Christopher G Atkeson, Roles of Knowledge in Motor Learning, Massachusetts Institute of Technology, Cambridge, MA, 1987
	7	Chcn, B.C., Creecy, R.H. and Appel, M.V. On error control in automated industry and occupation coding. In Proceedings American Statistical Association, Survey Methods Section. (1991), to appear.
	8	Church, K. A stochastic parts program and noun phrase parser for unrestricted text, Unpubished, cd manuscript, AT&T Bell Labs, Murray Hill, N.J., 1986.
	9	Church, K. ami Gale, W. Enhanced Good:Turing and Cal-Cal: Two new methods for estimating Probabilities of English Bigrams. AT&T Bell Laboratories, I989.
	10	Corbett, ,I.P. Encoding from Free Word Descriptions. Draft Mcmorandum, Bureau of the Census, 1972.
	11	Cover, T.M. and Hart, P.E. Nearest neighbor pattern classification. IEEE Trans. Inform. Theor, 13, (1967), 21-27.
	12	Creecy, R.H., Causey, B.D and AppeI, M,V. A bayesian ctassificadon approach to. automated industry and occupation coding., In Pro-. ceedings of American Statistical Axsocmtion, Statistical Computing Section, (1990).
	13	Dasrathy, B,V,, .Ed, Nearest: Neighbor (NN) Norms: NN Pattern: Classification Tchniques, IEEE Computer Society Press, 1990.
	14	Dempster, A.P, A generalization of' Bayesian. inference. J., Royal Statisti - cal Sot,, Series B. 30, (1968), 205- 1247.
	15	Fukunaga, K. and Flick, 'T.E. An optimal global nearest neighbor" metric. IEEE Trans. Pattern Anal. Machnine Intell., Vol. PAMI-6, 3 (May 1984), 314-318.
	16	Harman, D. How effctive is suffixing. J. Amer. Soc. Inform. Sci 42, 1 1997), 7-15.
	17	W D Hillis, The connection machine, MIT Press, Cambridge, MA, 1985
	18	Lorigny, J. Quid. A general automarie coding method. Surv, Method, 14, 52 (.1988), 289--298.
	19	Lyberg, L. and Dean. P. InternationaI review of approaches to automated industry and occupation coding. Presented at the Advanced computing in the Social Sciences conference, Wiliamsburg, Apr 1987, (1990).
	20	Robert T. O'Reagan, Computer-assigned codes from verbal responses, Communications of the ACM, v.15 n.6, p.455-459, June 1972 [doi>10.1145/361405.361419]
	21	Judea Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1988
	22	Quinlan, R, Learning efficient classification procedures and their application to chess end games, In R.S, Michalski, J. Carbonel, and R. Mitchell, Eds./, machnine Learnign: An Artificial Intelligence Approach. Tioga Publishinbg, Los Angeles, Calif. (1983), 463-482.
	23	Rumelhart, C., et. al. Parallel Distributed procesign. MIT Press, Cambridge, Mass. I986.
	24	Claude E. Shannon , Warren Weaver, A Mathematical Theory of Communication, University of Illinois Press, Champaign, IL, 1963
	25	Short, R.D. and Fukunaga, K. The optimal distance measure for nearest neighbor classificationn. IEEE Trans. Infor. Theor., Vol. IT-27, 5, (Sept., 198I), 622-627.
	26	Craig Stanfill , Brewster Kahle, Parallel free-text search on the connection machine system, Communications of the ACM, v.29 n.12, p.1229-1239, Dec. 1986 [doi>10.1145/7902.7907]
	27	Stanfill, C,. amt Waltz, D,L. The memory-nased reasoning paradigm. In Proceedings of Case-Based Reasoning Workshop, Clearwater Beach FL., (May 1988),414-424.
	28	Craig Stanfill , David Waltz, Toward memory-based reasoning, Communications of the ACM, v.29 n.12, p.1213-1228, Dec. 1986 [doi>10.1145/7902.7906]
	29	Waltz. D.L. Massively Parallel AI. In Proceedings of National Conference on AI., (AAAI '90), Boston, Mass., (Aug, 1990),
	30	Waltz, D.I. Memory-based Reasoning. In Eds. Arbib and .J.A. Robinson, Eds.. Natural and Artificial Parallel Computation, The MIT Press.. Cambridge, Mass.. (1990), '251- 276.
	31	D. H. Wolpert, Generalization, surface-fitting, and network structures, University of California at Santa Barbara, Santa Barbara, CA, 1990
	32	L. A. Zadeh, Knowledge Representation in Fuzzy Logic, IEEE Transactions on Knowledge and Data Engineering, v.1 n.1, p.89-100, March 1989 [doi>10.1109/69.43406]

CITED BY 28

	Brij Masand , Gordon Linoff , David Waltz, Classifying news stories using memory based reasoning, Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, p.59-65, June 21-24, 1992, Copenhagen, Denmark
	Fabrizio Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys (CSUR), v.34 n.1, p.1-47, March 2002
	Seishi Okamoto , Nobuhiro Yugami, Effects of domain characteristics on instance-based learning algorithms, Theoretical Computer Science, v.298 n.1, p.207-233, 4 April 2003
	Bradley P. Allen, Case-based reasoning: business applications, Communications of the ACM, v.37 n.3, p.40-42, March 1994
	Jody J. Daniels , Edwina L. Rissland, A case-based approach to intelligent information retrieval, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, p.238-245, July 09-13, 1995, Seattle, Washington, United States
	Yiming Yang, An Evaluation of Statistical Approaches to Text Categorization, Information Retrieval, v.1 n.1-2, p.69-90, 1999
	Paul Thompson, Automatic categorization of case law, Proceedings of the 8th international conference on Artificial intelligence and law, p.70-77, May 2001, St. Louis, Missouri, United States
	Edwina L. Rissland , Jody J. Daniels, A hybrid CBR-IR approach to legal information retrieval, Proceedings of the 5th international conference on Artificial intelligence and law, p.52-61, May 21-24, 1995, College Park, Maryland, United States
	Ben Wolin, Automatic classification in product catalogs, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland
	Fedro Domingos, Control-Sensitive Feature Selection for Lazy Learners, Artificial Intelligence Review, v.11 n.1-5, p.227-253, Feb. 1997
	David Waltz , Simon Kasif, On reasoning from data, ACM Computing Surveys (CSUR), v.27 n.3, p.356-359, Sept. 1995
	S. J. Smith , M. O. Bourgoin , K. Sims , H. L. Voorhees, Handwritten Character Classification Using Nearest Neighbor in Large Databases, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.16 n.9, p.915-919, September 1994
	Nick Cercone , Aijun An , Christine Chan, Rule-Induction and Case-Based Reasoning: Hybrid Architectures Appear Advantageous, IEEE Transactions on Knowledge and Data Engineering, v.11 n.1, p.166-174, January 1999
	Boi Faltings, Qualitative Models as Indices for Memory-Based Prediction, IEEE Expert: Intelligent Systems and Their Applications, v.12 n.3, p.47-53, May 1997
	Claire Cardie, A Cognitive Bias Approach to Feature Selection and Weighting for Case-Based Learners, Machine Learning, v.41 n.1, p.85-116, Oct. 2000
	Vijay S. Mookerjee, Debiasing Training Data for Inductive Expert System Construction, IEEE Transactions on Knowledge and Data Engineering, v.13 n.3, p.497-512, May 2001
	Jing Peng, Multi-class relevance feedback content-based image retrieval, Computer Vision and Image Understanding, v.90 n.1, p.42-67, April 2003
	Dietrich Wettschereck , David W. Aha , Takao Mohri, A Review and Empirical Evaluation of Feature Weighting Methods for aClass of Lazy Learning Algorithms, Artificial Intelligence Review, v.11 n.1-5, p.273-314, Feb. 1997
	Lu Zhang , Frans Coenen , Paul Leng, Setting attribute weights for k-NN based binary classification via quadratic programming, Intelligent Data Analysis, v.7 n.5, p.427-441, October 2003
	Mark Hall, A decision tree-based attribute weighting filter for naive Bayes, Knowledge-Based Systems, v.20 n.2, p.120-126, March, 2007
	David W. Aha, Editorial, Artificial Intelligence Review, v.11 n.1-5, p.7-10, Feb. 1997
	Mohammed Benkhalifa , Abdelhak Mouradi , Houssaine Bouyakhf, Integrating External Knowledge to Supplement Training Data in Semi-Supervised Learning for Text Categorization, Information Retrieval, v.4 n.2, p.91-113, July 2001
	Arie Ben-David , Eibe Frank, Accuracy of machine learning models versus "hand crafted" expert systems - A credit scoring case study, Expert Systems with Applications: An International Journal, v.36 n.3, p.5264-5271, April, 2009
	Mostafa Haghir Chehreghani , Morteza Haghir Chehreghani , Caro Lucas , Masoud Rahgozar , Euhanna Ghadimi, Efficient rule based structural algorithms for classification of tree structured data, Intelligent Data Analysis, v.13 n.1, p.165-188, January 2009
	Hiroaki Kitano, Challenges of massive parallelism, Proceedings of the 13th international joint conference on Artifical intelligence, p.813-834, August 28-September 03, 1993, Chambery, France
	Seishi Okamoto , Yugami Nobuhiro, An average-case analysis of the k-nearest neighbor classifier for noisy domains, Proceedings of the 15th international joint conference on Artifical intelligence, p.238-243, August 23-29, 1997, Nagoya, Japan
	Edwina L. Rissland , Jody J. Daniels, Using CBR to drive IR, Proceedings of the 14th international joint conference on Artificial intelligence, p.400-407, August 20-25, 1995, Montreal, Quebec, Canada
	Timo Steffens, Partial and vague knowledge for similarity measures, Proceedings of the 19th international joint conference on Artificial intelligence, p.21-26, July 30-August 05, 2005, Edinburgh, Scotland

INDEX TERMS

Primary Classification:
I. Computing Methodologies
I.2 ARTIFICIAL INTELLIGENCE
I.2.1 Applications and Expert Systems
Subjects: Natural language interfaces

General Terms:
Algorithms, Performance

Keywords:
automated system building, case-based reasoning, empirical learning, memory-based reasoning, textual database classification

REVIEW

"Richard L. Frautschi : Reviewer"

Developed for the US Census Bureau, the Parallel Automated Coding Expert (PACE) uses an empirical learning model called memory-based reasoning (MBR). It aims to replace the Automated Industry and Occupation Coding System (AIOCS) developed for more...

Collaborative Colleagues:

Robert H. Creecy: colleagues

Brij M. Masand: colleagues

Stephen J. Smith: colleagues

David L. Waltz: colleagues

Adobe Acrobat

QuickTime

Windows Media Player

Real Player