The effectiveness of machine learning techniques for predicting time to case disposition

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The effectiveness of machine learning techniques for predicting time to case disposition

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International Conference on Artificial Intelligence and Law archive
Proceedings of the 6th international conference on Artificial intelligence and law table of contents

Melbourne, Australia

Pages: 106 - 113

Year of Publication: 1997

ISBN:0-89791-924-6

Authors

Dawn Wilkins	University of Mississippi, 302 Weir Hall, University, MS
Krishnan Pillaipakkamnatt	Hofstra University, Hempstead, NY

Sponsors

SIGART: ACM Special Interest Group on Artificial Intelligence
IAAIL : Intl Asso for Artifical Intel & Law
UMIACS : U of MD Inst for Advanced Comp Studies
University of Melbourne : University of Melbourne

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 2, Downloads (12 Months): 12, Citation Count: 2

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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	Ron Kohavi , Dan Sommerfield , James Dougherty, Data Mining using MLC++, A Machine Learning Library in C++, Proceedings of the 8th International Conference on Tools with Artificial Intelligence (ICTAI '96), p.234, November 16-19, 1996
	3	Pat Langley , Herbert A. Simon, Applications of machine learning and rule induction, Communications of the ACM, v.38 n.11, p.54-64, Nov. 1995 [doi>10.1145/219717.219768]
	4	J. R. Quinlan, Induction of Decision Trees, Machine Learning, v.1 n.1, p.81-106 [doi>10.1023/A:1022643204877]
	5	C. Tata, J.N. Wilson, and N. Hutton. Representations of Knowledge and Discretionary Decision- Making by Decision-Support Systems: the Case of Judicial Sentencing. The Journal of Information, Law and Technology (JILT), 2, March 1996. http ://elj. warwick, ac. uk/elj/jilt/arti fint/~tata/.
	6	Bureau of Justice Statistics U.S. Department of Justice. Offender Based Transaction Statistics (OBTS): Alabama, Alaska, California, Idaho, Minnesota, Missouri, Nebraska, New Jersey, New York, Pennsylva~a, Vermont and Virginia. Computer file compiled by Regional Justice Information System, 1994.
	7	Sholom M. Weiss , Casimir A. Kulikowski, Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991

CITED BY 2

	Sasha Ivkovic , John Yearwood , Andrew Stranieri, Visualizing association rules for feedback within the legal system, Proceedings of the 9th international conference on Artificial intelligence and law, June 24-28, 2003, Scotland, United Kingdom
	Teresa Gonçalves , Paulo Quaresma, Is linguistic information relevant for the classification of legal texts?, Proceedings of the 10th international conference on Artificial intelligence and law, June 06-11, 2005, Bologna, Italy