| Description |
1 online resource |
| Series |
Intelligent systems reference library ; Volume 69 |
|
Intelligent systems reference library ; v. 69.
|
| Contents |
Support Vector Machines -- Evolutionary Algorithms -- Support Vector Machines and Evolutionary Algorithms |
| Summary |
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 achievinghigh accuracy, comprehensibility, reasonable runtime as well as unconstrained performance |
| Analysis |
engineering |
|
computational science |
|
kunstmatige intelligentie |
|
artificial intelligence |
|
Engineering (General) |
|
Techniek (algemeen) |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Algorithms.
|
|
Support vector machines.
|
|
Computer algorithms.
|
|
algorithms.
|
|
MATHEMATICS -- Numerical Analysis.
|
|
Ingénierie.
|
|
Computer algorithms
|
|
Algorithms
|
|
Support vector machines
|
| Form |
Electronic book
|
| Author |
Stoean, Ruxandra, author
|
| ISBN |
9783319069418 |
|
3319069411 |
|
