Extending and Unifying Formal Models for Machine Learning
Abstract
There has been a great deal of work on statistical pattern recognition, non-parametric estimation, and formal models of machine learning. Recent and classical work in these areas have provided fundamental results on the amount of data needed for classification, estimation, and prediction in a variety of non-parametric settings. The applicability of these paradigms is often limited by the assumptions on the data gathering mechanisms and the performance criteria. Our work has had two primary goals. The first is to investigate extensions and new models which give results useful in broader applications. The second goal is to apply these learning results to other areas such as signal/image processing, geometric reconstruction, and system identification. We have studied a variety of problems and have been able to relax assumptions required on the observed data as well as on the success criteria while still obtaining positive results. Our results have provided new insights into classical work and have also suggested a number of directions for further work.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 30, 1997
- Accession Number
- ADA328730
Entities
People
- Sanjeev R. Kulkarni
Organizations
- Princeton University