Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Evolution, Learning and Cognition
Evolution, Learning and Cognition
Discovering New Change Patterns in Object-Oriented Systems
WCRE '08 Proceedings of the 2008 15th Working Conference on Reverse Engineering
Complex system diagnosis through adaptive recognition
MAMECTIS'09 Proceedings of the 11th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Complex system diagnosis through adaptive recognition
MAMECTIS'09 Proceedings of the 11th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Adaptive categorization of complex system fault patterns
WSEAS TRANSACTIONS on SYSTEMS
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In this work, an approach to establish class membership conditions, using a labelled training set, is described. Most pattern recognition and classification approaches are based on identifying the 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. In other words, objects are classified as members of a particular class if they possess some features which make them distinguished from other objects present in the universe of objects. 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.