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

Ruxandra Stoean

ISBN 10: 3319069403 / ISBN 13: 9783319069401

Published by Springer International Publishing, 2014

About this Item

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 # 9783319069401

Bibliographic Details

• Title: Support Vector Machines and Evolutionary Algorithms for Classification : Single or Together?
• Publisher: Springer International Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 3319069403
• ISBN 13: 9783319069401
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 14D millimeters 241 millimeters width by 160 millimeters height by 14 millimeters depth
• Weight: 383 grams

About this title

Synopsis

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.