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Support Vector Machines and Evolutionary Algorithms for Classification Single or Together? /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
|
| Edición: | 1st ed. 2014. |
| Colección: | Intelligent Systems Reference Library,
69 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-06941-8 | ||
| 003 | DE-He213 | ||
| 005 | 20220112232725.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 140515s2014 sz | s |||| 0|eng d | ||
| 020 | |a 9783319069418 |9 978-3-319-06941-8 | ||
| 024 | 7 | |a 10.1007/978-3-319-06941-8 |2 doi | |
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| 100 | 1 | |a Stoean, Catalin. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Support Vector Machines and Evolutionary Algorithms for Classification |h [electronic resource] : |b Single or Together? / |c by Catalin Stoean, Ruxandra Stoean. |
| 250 | |a 1st ed. 2014. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2014. | |
| 300 | |a XVI, 122 p. 31 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Intelligent Systems Reference Library, |x 1868-4408 ; |v 69 | |
| 505 | 0 | |a Support Vector Machines -- Evolutionary Algorithms -- Support Vector Machines and Evolutionary Algorithms. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 700 | 1 | |a Stoean, Ruxandra. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319069425 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319069401 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319382432 |
| 830 | 0 | |a Intelligent Systems Reference Library, |x 1868-4408 ; |v 69 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-06941-8 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||