Description
Learning with Partially Labeled and Interdependent Data, Softcover reprint of the original 1st ed. 2015
Authors: Amini Massih-Reza, Usunier Nicolas
Language: EnglishSubjects for Learning with Partially Labeled and Interdependent Data:
Approximative price 52.74 €
In Print (Delivery period: 15 days).
Add to cart the print on demand of Amini Massih-Reza, Usunier NicolasPublication date: 10-2016
Support: Print on demand
Approximative price 52.74 €
In Print (Delivery period: 15 days).
Add to cart the book of Amini Massih-Reza, Usunier NicolasPublication date: 05-2015
106 p. · 15.5x23.5 cm · Hardback
Description
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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.
Introduction.- Introduction to learning theory.- Semi-supervised learning.- Learning with interdependent data.
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