| Document Type
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BL
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| Record Number
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605955
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| Doc. No
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b435174
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| Main Entry
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Woźniak, Michał
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| Title & Author
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Hybrid classifiers : : methods of data, knowledge, and classifier combination /\ Michał Woźniak
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| Series Statement
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Studies in Computational Intelligence,; v.519
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| Page. NO
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1 online resource (xvi, 217 pages) :: illustrations
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| ISBN
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3642409970 (electronic bk.)
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: 9783642409974 (electronic bk.)
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9783642409967
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| Bibliographies/Indexes
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Includes bibliographical references and index
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| Contents
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Data and knowledge hybridization -- Classifier hybridization -- Chosen applications of hybrid classifiers -- Conclusions
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| Abstract
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This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification
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| Subject
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Machine learning
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Engineering
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| Subject
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Artificial Intelligence (incl. Robotics)
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| Subject
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Computational Intelligence
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| Dewey Classification
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006.3
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| LC Classification
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Q325.5
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| Added Entry
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Ohio Library and Information Network
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