Learning with Partially Labeled and Interdependent Data
Autor Massih-Reza Amini, Nicolas Usunieren Limba Engleză Paperback – 9 oct 2016
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.
Toate formatele și edițiile | Preț | Express |
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Paperback (1) | 321.44 lei 6-8 săpt. | |
Springer International Publishing – 9 oct 2016 | 321.44 lei 6-8 săpt. | |
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Springer International Publishing – 21 mai 2015 | 327.50 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783319353906
ISBN-10: 331935390X
Pagini: 119
Ilustrații: XIII, 106 p. 12 illus.
Dimensiuni: 155 x 235 x 7 mm
Greutate: 0.18 kg
Ediția:Softcover reprint of the original 1st ed. 2015
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 331935390X
Pagini: 119
Ilustrații: XIII, 106 p. 12 illus.
Dimensiuni: 155 x 235 x 7 mm
Greutate: 0.18 kg
Ediția:Softcover reprint of the original 1st ed. 2015
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Introduction.- Introduction to learning theory.- Semi-supervised learning.- Learning with interdependent data.
Caracteristici
Presents an overview of statistical learning theory Analyzes two machine learning frameworks, semi-supervised learning with partially labeled data and learning with interdependent data Outlines how these frameworks can support emerging machine learning applications Includes supplementary material: sn.pub/extras