A neural network for speedy trials

Ray R. Hashemi, Therese M. Schafer, William G. Hinson, John R. Talburt

Research output: Contribution to book or proceedingConference articlepeer-review

Abstract

In recent years, the case loads of judges have increased, while speedy trial laws place a time limit between the defendant's arrest and trial dates. Because of this time constraint, it seems that for minor cases, judges pass sentences based on a set of certain factors (patterns) not based on the individual merits of each case. Patterns may be learned by a neural network. In this paper, we investigate the credibility of the neural network approach as a viable tool in the sentencing process and we show its superiority over the ID3 approach.

Original languageEnglish
Title of host publicationProceedings of the 1996 ACM Symposium on Applied Computing, SAC 1996
EditorsJanice H. Carroll, K. M. George, Jim Hightower, Dave Oppenheim
PublisherAssociation for Computing Machinery
Pages468-472
Number of pages5
ISBN (Electronic)0897918207
DOIs
StatePublished - Feb 18 1996
Event1996 ACM Symposium on Applied Computing, SAC 1996 - Philadelphia, United States
Duration: Feb 17 1996Feb 19 1996

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F128723

Conference

Conference1996 ACM Symposium on Applied Computing, SAC 1996
Country/TerritoryUnited States
CityPhiladelphia
Period02/17/9602/19/96

Keywords

  • Computer in courts
  • Computerized sentencing
  • Intelligent systems
  • Neural networks
  • Speedy trial

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