179,468 research outputs found

    The theory of classification part 12: building the class hierarchy

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    The construction of specific object instances and the development of simple class hierarchy are discussed. The class hierarchy development includes recognizable classes, with mixtures of default, abstract and concrete methods. Generators that are extended into flexible object creation functions with initialization values passed to superclass functions, mimicking the behavior of real object-oriented languages. It is shown that the classes that are derived by inheritance give rise to objects of exact types, which match the expected interfaces of their superclasses and the separately-declared interfaces

    Automated Protein Structure Classification: A Survey

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    Classification of proteins based on their structure provides a valuable resource for studying protein structure, function and evolutionary relationships. With the rapidly increasing number of known protein structures, manual and semi-automatic classification is becoming ever more difficult and prohibitively slow. Therefore, there is a growing need for automated, accurate and efficient classification methods to generate classification databases or increase the speed and accuracy of semi-automatic techniques. Recognizing this need, several automated classification methods have been developed. In this survey, we overview recent developments in this area. We classify different methods based on their characteristics and compare their methodology, accuracy and efficiency. We then present a few open problems and explain future directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront

    From Data Topology to a Modular Classifier

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    This article describes an approach to designing a distributed and modular neural classifier. This approach introduces a new hierarchical clustering that enables one to determine reliable regions in the representation space by exploiting supervised information. A multilayer perceptron is then associated with each of these detected clusters and charged with recognizing elements of the associated cluster while rejecting all others. The obtained global classifier is comprised of a set of cooperating neural networks and completed by a K-nearest neighbor classifier charged with treating elements rejected by all the neural networks. Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given
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