Support Vector Machines and Evolutionary Algorithms for Classification: Single or Together? (Intelligent Systems Reference Library, 69)
Stoean, Catalin; Stoean, Ruxandra
ISBN 10: 3319069403 ISBN 13: 9783319069401
Published by Springer, 2014
New
Hardcover
From
Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
AbeBooks Seller since 25 March 2015
This specific item is no longer available.
About this Item
Description:
In. Seller Inventory # ria9783319069401_new
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.
From the Back Cover:
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.
"About this title" may belong to another edition of this title.
Bibliographic Details
Title: Support Vector Machines and Evolutionary ...
Publisher: Springer
Publication Date: 2014
Binding: Hardcover
Condition: New