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Support Vector Machines and Evolutionary Algorithms for Classification: Single or Together? (Intelligent Systems Reference Library, 69) - Softcover
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When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions. The question raised in this book is how can this ‘masked hero’ be made more comprehensible and friendly to the public: provide a surrogate model for its hidden optimization engine, replace the method completely or appoint a more friendly approach to tag along and offer the much desired explanations? Evolutionary algorithms can do all these and this book presents such possibilities of achieving high accuracy, comprehensibility, reasonable runtime as well as unconstrained performance.
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When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions. The question raised in this book is how can this ‘masked hero’ be made more comprehensible and friendly to the public: provide a surrogate model for its hidden optimization engine, replace the method completely or appoint a more friendly approach to tag along and offer the much desired explanations? Evolutionary algorithms can do all these and this book presents such possibilities of achieving high accuracy, comprehensibility, reasonable runtime as well as unconstrained performance.
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Support Vector Machines and Evolutionary Algorithms for Classification: Single or Together? (Intelligent Systems Reference Library, 69)
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Support Vector Machines and Evolutionary Algorithms for Classification
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Berlin Springer International Publishing Springer Sep 2016, 2016
ISBN 10: 3319382438
ISBN 13: 9783319382432
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions. The question raised in this book is how can this 'masked hero' be made more comprehensible and friendly to the public: provide a surrogate model for its hidden optimization engine, replace the method completely or appoint a more friendly approach to tag along and offer the much desired explanations Evolutionary algorithms can do all these and this book presents such possibilities of achieving high accuracy, comprehensibility, reasonable runtime as well as unconstrained performance. 122 pp. Englisch. Seller Inventory # 9783319382432
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Support Vector Machines and Evolutionary Algorithms for Classification: Single or Together? (Intelligent Systems Reference Library)
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Support Vector Machines and Evolutionary Algorithms for Classification
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Springer International Publishing, 2016
ISBN 10: 3319382438
ISBN 13: 9783319382432
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Guides the reader from single methodologies, like support vector machines and evolutionary algorithms, to hybridization at different levels between the two, showing the benefits and drawbacks of eachContains new approaches to classification person. Seller Inventory # 385704389
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Support Vector Machines and Evolutionary Algorithms for Classification: Single or Together? (Intelligent Systems Reference Library)
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Support Vector Machines and Evolutionary Algorithms for Classification: Single or Together? (Intelligent Systems Reference Library)
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Support Vector Machines and Evolutionary Algorithms for Classification : Single or Together?
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Springer, Berlin, Springer International Publishing, Springer, 2016
ISBN 10: 3319382438
ISBN 13: 9783319382432
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions. The question raised in this book is how can this 'masked hero' be made more comprehensible and friendly to the public: provide a surrogate model for its hidden optimization engine, replace the method completely or appoint a more friendly approach to tag along and offer the much desired explanations Evolutionary algorithms can do all these and this book presents such possibilities of achieving high accuracy, comprehensibility, reasonable runtime as well as unconstrained performance. Seller Inventory # 9783319382432
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Support Vector Machines and Evolutionary Algorithms for Classification: Single or Together?
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Springer-Verlag New York Inc, 2016
ISBN 10: 3319382438
ISBN 13: 9783319382432
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Paperback. Condition: Brand New. reprint edition. 140 pages. 9.25x6.10x0.32 inches. In Stock. Seller Inventory # x-3319382438
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