Recognition of class membership by means of weak, statistically dependent features /

A method of automatic classification is developed for the case in which the features used to determine the class of an unknown object x are individually weak. The features are weak in the sense that any subset of the universe defined by a single feature value of x contains many objects belonging to a class different from that of x. The classes are defined by a small collection of examples, which are objects whose class membership and feature values are known. The basic problem in classification by example is the estimation of probabilities from a small number of examples. Ideally, the class of x should be determined by estimating the class probabilities in the subset J defined by the conjunction of all of the feature values of x. But J usually will contain no examples on which to base an estimate of the class probabilities. The recognition procedure offered here employs a subset D defined by the conjunction of some, but not all, of the feature values of x. It is postulated that the features are statistically dependent in such a way that the particular kind of dependence exhibited by the features defining D can be used to infer the class of x. This recognition procedure includes a hypothesis-testing procedure in which the competing hypotheses are related to the dependence of the features defining D and are only indirectly related to the classes to which x could possibly belong. In this approach the class of x can be decided without requiring that D contain any examples. These principles are demonstrated by experiments in character recognition.Includes bibliographic references (page 39).A method of automatic classification is developed for the case in which the features used to determine the class of an unknown object x are individually weak. The features are weak in the sense that any subset of the universe defined by a single feature value of x contains many objects belonging to a class different from that of x. The classes are defined by a small collection of examples, which are objects whose class membership and feature values are known. The basic problem in classification by example is the estimation of probabilities from a small number of examples. Ideally, the class of x should be determined by estimating the class probabilities in the subset J defined by the conjunction of all of the feature values of x. But J usually will contain no examples on which to base an estimate of the class probabilities. The recognition procedure offered here employs a subset D defined by the conjunction of some, but not all, of the feature values of x. It is postulated that the features are statistically dependent in such a way that the particular kind of dependence exhibited by the features defining D can be used to infer the class of x. This recognition procedure includes a hypothesis-testing procedure in which the competing hypotheses are related to the dependence of the features defining D and are only indirectly related to the classes to which x could possibly belong. In this approach the class of x can be decided without requiring that D contain any examples. These principles are demonstrated by experiments in character recognition.Research supported by Aerospace Medical Division, Air Force Systems Command, United States Air Force; report prepared by Radio Corporation of America, RCA Laboratories, under contract no.Mode of access: Internet

Development of megasonic cleaning for silicon wafers : quarterly report no. 4 /

"April 1, 1980."Includes bibliographical references.Work performed under contract no. ;Mode of access: Internet

Low-cost epitaxial techniques for solar-cell fabrication : quarterly technical progress report no. 3 for March 25 - June 24, 1980 /

"Work Performed Under Contract No. AC02-77CH00178.""July1980.""Prepared under Subcontract No. XS-9-8274."Includes bibliographical references (page 29).Mode of access: Internet

Automated array assembly, phase II : quarterly report no. 6, June 1979 /

"June 1979."Prepared under contract no. 954868 for Jet Propulsion Laboratory, California Institute of Technology.Includes bibliographical references (page 30).Work performed under contract no. ;Mode of access: Internet

Automated array assembly, phase II : quarterly report no. 7, October 1979 /

"October 1979."Prepared under contract no. 954868 for Jet Propulsion Laboratory, California Institute of Technology.Includes bibliographical references (page 42).Work performed under contract no. ;Mode of access: Internet

Automated array assembly.

Issued Dec. 1977.Author: R.V. D'Aiello.Final report, February 3, 1976-November 2, 1977.Work performed under contract no.Mode of access: Internet

Epitaxial silicon growth for solar cells /

"January 1978."Quarterly report no. 1.Mode of access: Internet

Development and evaluation of die materials for use in the growth of silicon ribbons by the inverted ribbon growth process, task II, LSSA project : quarterly report no. 1, December 1977 /

"PRRL-77-CR-53.""December 1977."Work performed for the Jet Propulsion Laboratory, California Institute of Technology by RCA Laboratories, under contract no. NAS-7-100-954901, for the Department of Energy.Includes bibliographical references (page 15).Work performed under contract no. ;Mode of access: Internet