Graph-based semi-supervised learning /

other_versions_link

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of...

Full description

Bibliographic Details
Main Authors: Subramanya, Amarnag (Author), Talukdar, Partha Pratim (Author)
Format: eBook
Language:English
Published: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.
Series:Synthesis digital library of engineering and computer science.
Synthesis lectures on artificial intelligence and machine learning ; # 29.
Subjects:
Machine learning > Graphic methods.
Supervised learning (Machine learning)
graph Laplacian
graph-based learning
graph-based semi-supervised learning
inductive learning
label propagation
manifold learning
nonparametric learning
scalable machine learning
semi-supervised learning
transductive learning
Online Access:Abstract with links to full text
Description
Summary:While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of- the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.
Item Description:Part of: Synthesis digital library of engineering and computer science.
Series from website.
Physical Description:1 PDF (xiii, 111 pages) : illustrations.
Also available in print.
Format:Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Bibliography:Includes bibliographical references (pages 97-108) and index.
ISBN:9781627052023
ISSN:1939-4616 ;
Access:Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Similar Items