A NONPARAMETRIC APPROACH TO PATTERN RECOGNITION. PART II. THE NON-DISJOINT CASE
In Part I of this paper, (AD-664 218), a nonparametric discrimination technique was proposed. It was shown there that when perfect discrimination was possible, this technique achieved perfection and in certain cases achieved it with a finite learning phase. In this report, the technique is modified...
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| creator | Owen,Joel Henrichon,Ernest G. , Jr |
| description | In Part I of this paper, (AD-664 218), a nonparametric discrimination technique was proposed. It was shown there that when perfect discrimination was possible, this technique achieved perfection and in certain cases achieved it with a finite learning phase. In this report, the technique is modified to include the case when perfect discrimination is not possible. It is shown that this procedure yields results which converge in probability to the optimal decision boundaries determined by the likelihood ratio method. (Author)
See also Part 1, AD-664 218. |
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See also Part 1, AD-664 218.</description><subject>ALGORITHMS</subject><subject>ARTIFICIAL INTELLIGENCE</subject><subject>AUTOMATA</subject><subject>Bionics</subject><subject>Cybernetics</subject><subject>DECISION THEORY</subject><subject>INFORMATION THEORY</subject><subject>LEARNING MACHINES</subject><subject>PATTERN RECOGNITION</subject><subject>PROBABILITY</subject><subject>STATISTICAL ANALYSIS</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>1968</creationdate><recordtype>report</recordtype><sourceid>1RU</sourceid><recordid>eNqFi7EKwkAMQLs4iPoHDvmBFqVF6Biu0YtgcqTZi2iFgrj0_h8V3J0evMdbFoogKgkNL-TGATAlUwwRXCGhO5mAUdCTsLNK9ZHmwFyBR_q-Zcf9WVkcAva0LhaP63MeNz-uiu2RPMTynqfbMOfpNeYBu92hrfdNU__Jb1c6K4g</recordid><startdate>19681004</startdate><enddate>19681004</enddate><creator>Owen,Joel</creator><creator>Henrichon,Ernest G. , Jr</creator><scope>1RU</scope><scope>BHM</scope></search><sort><creationdate>19681004</creationdate><title>A NONPARAMETRIC APPROACH TO PATTERN RECOGNITION. PART II. THE NON-DISJOINT CASE</title><author>Owen,Joel ; Henrichon,Ernest G. , Jr</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-dtic_stinet_AD06931443</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>1968</creationdate><topic>ALGORITHMS</topic><topic>ARTIFICIAL INTELLIGENCE</topic><topic>AUTOMATA</topic><topic>Bionics</topic><topic>Cybernetics</topic><topic>DECISION THEORY</topic><topic>INFORMATION THEORY</topic><topic>LEARNING MACHINES</topic><topic>PATTERN RECOGNITION</topic><topic>PROBABILITY</topic><topic>STATISTICAL ANALYSIS</topic><toplevel>online_resources</toplevel><creatorcontrib>Owen,Joel</creatorcontrib><creatorcontrib>Henrichon,Ernest G. , Jr</creatorcontrib><creatorcontrib>INFORMATION RESEARCH ASSOCIATES INC WALTHAM MASS</creatorcontrib><collection>DTIC Technical Reports</collection><collection>DTIC STINET</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Owen,Joel</au><au>Henrichon,Ernest G. , Jr</au><aucorp>INFORMATION RESEARCH ASSOCIATES INC WALTHAM MASS</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><btitle>A NONPARAMETRIC APPROACH TO PATTERN RECOGNITION. PART II. THE NON-DISJOINT CASE</btitle><date>1968-10-04</date><risdate>1968</risdate><abstract>In Part I of this paper, (AD-664 218), a nonparametric discrimination technique was proposed. It was shown there that when perfect discrimination was possible, this technique achieved perfection and in certain cases achieved it with a finite learning phase. In this report, the technique is modified to include the case when perfect discrimination is not possible. It is shown that this procedure yields results which converge in probability to the optimal decision boundaries determined by the likelihood ratio method. (Author)
See also Part 1, AD-664 218.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | ALGORITHMS ARTIFICIAL INTELLIGENCE AUTOMATA Bionics Cybernetics DECISION THEORY INFORMATION THEORY LEARNING MACHINES PATTERN RECOGNITION PROBABILITY STATISTICAL ANALYSIS |
| title | A NONPARAMETRIC APPROACH TO PATTERN RECOGNITION. PART II. THE NON-DISJOINT CASE |
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