Support Vector Machines and Evolutionary Algorithms for Classification

Catalin Stoean

ISBN 10: 3319382438 / ISBN 13: 9783319382432

Published by Berlin Springer International Publishing Springer Sep 2016, 2016

About this Item

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

Bibliographic Details

• Title: Support Vector Machines and Evolutionary Algorithms for Classification
• Publisher: Berlin Springer International Publishing Springer Sep 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 3319382438
• ISBN 13: 9783319382432
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 154H x 7D millimeters 234 millimeters width by 154 millimeters height by 7 millimeters depth
• Weight: 225 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.