Support Vector Machines and Evolutionary Algorithms for Classification Single or Together?
Language: English
Published by Springer, 2014
Series: Book 42 of 188 - Intelligent Systems Reference Library
- Hardcover
- New
Condition: New
£ 114.91
Quantity: 2 available
Add to basketItem description from seller
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. Sprache: Englisch Gewicht in Gramm: 331.
Seller Inventory # IB-334776
- Title
- Support Vector Machines and Evolutionary Algorithms for Classification Single or Together?
- Author
- Ruxandra Stoean, Catalin Stoean
- Publisher
- Springer
- Publication year
- 2014
- Condition
- Neu
- Binding
- Gebundene Ausgabe
- Language
- English
- ISBN 10
- 3319069403
- ISBN 13
- 9783319069401
- Item weight
- 331 grams
- Series
- Book 42 of 188: Intelligent Systems Reference Library
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.
"Synopsis" may belong to another edition of this title.
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 the title" may belong to another edition of this title.
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