Support Vector Machines and Evolutionary Algorithms for Classification Single or Together? /

Detalles Bibliográficos
Autor Principal: Stoean, Catalin
Otros autores o Colaboradores: Stoean, Ruxandra
Formato: Libro
Lengua:inglés
Datos de publicación: Cham : Springer International Publishing : Imprint: Springer, 2014.
Series:Intelligent Systems Reference Library, 69
Temas:
Engineering.
Computational intelligence.
Artificial Intelligence (incl. Robotics).
Acceso en línea:http://dx.doi.org/10.1007/978-3-319-06941-8
Resumen: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 â_~masked heroâ_T 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.
Descripción Física:xvi, 122 p. : il.
ISBN:9783319069418
ISSN:1868-4394 ;
DOI:10.1007/978-3-319-06941-8

MARC

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