Compound Decision Procedures for Pattern Classification

Item

Title
Compound Decision Procedures for Pattern Classification
Date
1967
Index Abstract
Not Available
Photo Quality
Not Needed
Report Number
AMRL TR 67-10
Creator
Abend, Kenneth
Corporate Author
Philco-Ford Corporation
Laboratory
Biomedical Laboratory
Extent
107
Identifier
AD0667570
Access Rights
Distribution of this document is unlimited. It may be released to the Clearinghouse, Department of Commerce, for sale to the general public.
Distribution Classification
1
Contract
AF 33(615)-2966
DoD Project
7233 - Biological Information Handling Systems and Their Functional Analogs
DoD Task
723305 - Theory of Information Handling
DTIC Record Exists
No
Distribution Change Authority Correspondence
None
Distribution Conflict
No
Abstract
Compound decision theory is shown to be powerful as a general theoretical framework for pattern recognition, leading to nonparametric methods, methods of threshold adjustment, and methods for taking context into account. The finite-sample-size performance of the Fix-Hodges nearest-neighbor nonparametric classification procedure is derived for independent binary patterns. The optimum (Bayes) sequential compound decision procedure, for known distributions and dependent states of nature is derived. When the states of nature form a Markov chain, the procedure is recursive, easily implemented, and immediately applicable to the use of context. A similar procedure, in which a decision depends on previous observations only through the decision about the preceding state of nature, can (when the populations are not well separated) yield results significantly worse than a procedure that does not depend on previous observations at all. When the populations are well separated, however, an improvement almost equal to that of the optimum sequential rule is achieved.
Report Availability
Full text available
Date Issued
1967-12
Provenance
RAF Centre of Aviation Medicine
Type
report
Format
1 online resource
Media
AMRLTR67-010.pdf