Accuracy and Efficiency in Pattern Classification.
Abstract
A pattern classification scheme which is grounded in classical probability theory may be associated with confidence internvals that represent an estimate of the predictive capability of the scheme. As a practical matter, realistic allocations of data acquisition and processing resources may severely constrain acceptable levels of predictability. We examine some of the basic assumptions which underlie the standard statistical techniques. In particular, we show that fuzzy logic effectively produces conservative estimates for the conditional probability of the union of sets since, in that case, it neglects information related to the intersection. We propose that such neglect can be remedied, at a computational cost, without resorting explicitly to the usual procedure of integrating over irregularly shaped volumes. To this end, we introduce a class of probability density distributions which possesses (hyper) rectangular contour. Explicit formulas for the normalization constant and the probability of error are then derived for typical distributions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1979
- Accession Number
- ADA066511
Entities
People
- S. Berkowitz