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Testing of the Support Vector Machine for Binary-Class ClassificationThe Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results
Document ID
20150006897
Acquisition Source
Jet Propulsion Laboratory
Document Type
Other
External Source(s)
Authors
Scholten, Matthew
(California State Univ. Long Beach, CA, United States)
Date Acquired
April 29, 2015
Publication Date
August 1, 2011
Subject Category
Computer Systems
Optics
Computer Programming And Software
Distribution Limits
Public
Copyright
Other
Keywords
autonomous target recognition systemr
grayscale optical correlator (GOC)mr

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