The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 29-669
Full Name: Seyed Shahrestani
Position: Senior Lecturer
Age: ON
Sex: Male
Address: Locked Bag 1797
Country: AUSTRALIA
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E-mail address: Seyed@computer.org
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Title of the Paper: Adaptive Categorization in Complex Systems
Authors as they appear in the Paper: Seyed Shahrestani
Email addresses of all the authors: Seyed@computer.org
Number of paper pages: 10
Abstract: A fast and reliable method for categorization of patterns that may be encountered in complex systems is described. Most pattern recognition and classification approaches are founded on discovering the connections and similarities between the members of each class. In this work, a different view of classification is presented. The classification is based on identification of distinctive features of patterns. It will be shown that the members of different classes have different values for some or all of such features. The paper will also show that by making use of the distinctive features and their corresponding values, classification of all patterns, even for complex systems, can be accomplished. The classification process does not rely on any heuristic rules. In this process, patterns are grouped together in such a way that their distinctive features can be explored. Such features are then used for identification purposes.
Keywords: Adaptive recognition, Categorization and classification, Distinctive Features, Complex Systems, Feature Extraction, Negative Recognition
EXTENSION of the file: .pdf
Special (Invited) Session: Classification in Complex Systems through Negative Recognition
Organizer of the Session: 624-106
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