Insights from jurisprudence for machine learning in law
- Authors: Stranieri, Andrew , Zeleznikow, John
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 85-98
- Full Text: false
- Reviewed:
- Description: The central theme of this chapter is that the application of machine learning to data in the legal domain involves considerations that derive from jurisprudential assumptions about the nature of legal reasoning. Jurisprudence provides a unique resource for machine learning in that, for over one hundred years, significant thinkers have advanced concepts including open texture and discretion. These concepts inform and guide applications of machine learning to law.
Knowledge discovery from legal databases
- Authors: Stranieri, Andrew , Zeleznikow, John
- Date: 2005
- Type: Text , Book
- Full Text: false
- Reviewed:
- Description: A1
- Description: 2003000833
Tools for placing legal decision support systems on the world wide web
- Authors: Stranieri, Andrew , Yearwood, John , Zeleznikow, John
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at Eighth International Conference on Artificial Intelligence and Law, ICAIL 2001, St. Louis, USA : 21st-25th May 2001
- Full Text: false
- Description: 2003003944