This book provides a fundamentally new approach to pattern recognition in which objects are characterized by relations to other objects instead of by using features or models. This 'dissimilarity representation' bridges the gap between the traditionally opposing approaches of statistical and structural pattern recognition.Physical phenomena, objects and events in the world are related in various and often complex ways. Such relations are usually modeled in the form of graphs or diagrams. While this is useful for communication between experts, such representation is difficult to combine and integrate by machine learning procedures. However, if the relations are captured by sets of dissimilarities, general data analysis procedures may be applied for analysis.With their detailed description of an unprecedented approach absent from traditional textbooks, the authors have crafted an essential book for every researcher and systems designer studying or developing pattern recognition systems.
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Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition. Codice articolo 4046522
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Da: Revaluation Books, Exeter, Regno Unito
Hardcover. Condizione: Brand New. illustrated edition. 636 pages. 9.50x6.75x1.50 inches. In Stock. Codice articolo __9812565302
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: moluna, Greven, Germania
Gebunden. Condizione: New. Provides an approach to pattern recognition in which objects are characterized by relations to other objects instead of by using features or models. This book is intended for researchers and systems designers studying or developing pattern recognition syste. Codice articolo 599238306
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Da: Ria Christie Collections, Uxbridge, Regno Unito
Condizione: New. In. Codice articolo ria9789812565303_new
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Da: AHA-BUCH GmbH, Einbeck, Germania
Buch. Condizione: Neu. Neuware - This book provides a fundamentally new approach to pattern recognition in which objects are characterized by relations to other objects instead of by using features or models. This 'dissimilarity representation' bridges the gap between the traditionally opposing approaches of statistical and structural pattern recognition. Physical phenomena, objects and events in the world are related in various and often complex ways. Such relations are usually modeled in the form of graphs or diagrams. While this is useful for communication between experts, such representation is difficult to combine and integrate by machine learning procedures. However, if the relations are captured by sets of dissimilarities, general data analysis procedures may be applied for analysis. With their detailed description of an unprecedented approach absent from traditional textbooks, the authors have crafted an essential book for every researcher and systems designer studying or developing pattern recognition syste. Codice articolo 9789812565303
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