Overview
- Guides the reader from single methodologies, like support vector machines and evolutionary algorithms, to hybridization at different levels between the two, showing the benefits and drawbacks of each
- Contains new approaches to classification personally developed and tested by the authors based on evolutionary algorithms and support vector machines
- Fills the gaps between theoretical classification and the practical issues revolving around computer aided diagnosis
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 69)
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Table of contents (8 chapters)
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Front Matter
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Support Vector Machines
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Front Matter
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Evolutionary Algorithms
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Front Matter
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Support Vector Machines and Evolutionary Algorithms
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Front Matter
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Back Matter
Keywords
About this book
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.
Reviews
From the book reviews:
“This book is intended for scholars, students, and developers who are interested and engaged in machine learning approaches and, particularly, in classification approaches via support vector machines (SVMs). … the book is recommended to those with advanced knowledge in machine learning and, in particular, SVMs as a hypothesis modeling classification approach. … the presentation of each topic remains systematic and the authors make good use of examples throughout the book.” (Epaminondas Kapetanios, Computing Reviews, November, 2014)Authors and Affiliations
Bibliographic Information
Book Title: Support Vector Machines and Evolutionary Algorithms for Classification
Book Subtitle: Single or Together?
Authors: Catalin Stoean, Ruxandra Stoean
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-319-06941-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-06940-1Published: 13 June 2014
Softcover ISBN: 978-3-319-38243-2Published: 17 September 2016
eBook ISBN: 978-3-319-06941-8Published: 15 May 2014
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XVI, 122
Number of Illustrations: 31 b/w illustrations