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Support Vector Machines and Evolutionary Algorithms for Classification [electronic resource] : Single or Together? / by Catalin Stoean, Ruxandra Stoean.

By: Stoean, Catalin [author.].
Contributor(s): Stoean, Ruxandra [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Intelligent Systems Reference Library: 69Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XVI, 122 p. 31 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319069418.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Support Vector Machines -- Evolutionary Algorithms -- Support Vector Machines and Evolutionary Algorithms.
In: Springer eBooksSummary: 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 -- Evolutionary Algorithms -- Support Vector Machines and Evolutionary Algorithms.

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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