Proceedings of the International Conference (7th) on Machine Learning Held in Austin, Texas on 21-23 June 1990
Abstract
Machine learning is a study of computational methods for acquiring knowledge and improving problem solving ability. Because of the breadth of this charter, machine learning includes a wide range of topics. This volume collects research results from twelve areas of machine learning which were represented at the Seventh International Conference on Machine Learning, held June 21-23, 1990 at the University of Texas in Austin. The 165 technical papers submitted to the conference provide evidence that machine learning continues to mature and evolve. New areas of active research, such as robot learning, have emerged, presenting challenging new problems and applications. Furthermore, many papers described research that cuts across traditional boundaries in machine learning and synthesizes disparate results. Keywords: Clustering, Genetic algorithms, Learning and planning, Robot learning, Language learning, Constructive induction.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 23, 1990
- Accession Number
- ADA224409
Entities
People
- Bruce W. Porter
- Ray J. Mooney
Organizations
- University of Texas at Austin