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Learning with Partially Labeled and Interdependent Data
This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Edición: | 1st ed. 2015. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-319-15726-9 | ||
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| 005 | 20220112104406.0 | ||
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| 008 | 150507s2015 sz | s |||| 0|eng d | ||
| 020 | |a 9783319157269 |9 978-3-319-15726-9 | ||
| 024 | 7 | |a 10.1007/978-3-319-15726-9 |2 doi | |
| 050 | 4 | |a Q334-342 | |
| 050 | 4 | |a TA347.A78 | |
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| 100 | 1 | |a Amini, Massih-Reza. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Learning with Partially Labeled and Interdependent Data |h [electronic resource] / |c by Massih-Reza Amini, Nicolas Usunier. |
| 250 | |a 1st ed. 2015. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2015. | |
| 300 | |a XIII, 106 p. 12 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 | ||
| 505 | 0 | |a Introduction -- Introduction to learning theory -- Semi-supervised learning -- Learning with interdependent data. | |
| 520 | |a This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus do not meet the demands of many web-related tasks. Later chapters deal with the development of learning methods for ranking entities in a large collection with respect to precise information needed. In some cases, learning a ranking function can be reduced to learning a classification function over the pairs of examples. The book proves that this task can be efficiently tackled in a new framework: learning with interdependent data. Researchers and professionals in machine learning will find these new perspectives and solutions valuable. Learning with Partially Labeled and Interdependent Data is also useful for advanced-level students of computer science, particularly those focused on statistics and learning. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Statistics . | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. |
| 700 | 1 | |a Usunier, Nicolas. |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 9783319157252 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319157276 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319353906 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-15726-9 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||