Image of Learning with Partially Labeled and Interdependent Data
Bookmark Share

Text

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 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.

Availability

No copy data

Detail Information
Series Title
-
Call Number
-
Publisher
Cham : .,
Collation
-
Language
English
ISBN/ISSN
978-3-319-15726-9
Classification
NONE
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Chemistry and Earth Sciences
Specific Detail Info
-
Statement of Responsibility
Other Information
Cataloger
Kholif Basri
Source
https://link.springer.com/book/10.1007/978-3-319-15726-9
Validator
-
Other version/related

No other version available

File Attachment
Comments
You must be logged in to post a comment