
More Than Semi-Supervised Learning
A unified view on Learning with Labeled and Unlabeled Data
LAP Lambert Academic Publishing
Published on 28. December 2010
Book
Paperback/Softback
132 pages
978-3-8433-7910-6 (ISBN)
Description
Semi-supervised learning (SSL) has grown into an important research area in machine learning, motivated by the fact that human labeling is expensive while unlabeled data are relatively easy to obtain. A basic assumption in traditional SSL is that unlabeled data and labeled data share the same distribution. However, this assumption may be incorrect when unlabeled data have a shifted covariance, or come from a related but different domain, or contain irrelevant data. With the divergence of the distribution of unlabeled data, very little academic literature exists on how to choose or adapt machine learning algorithms to different settings of unlabeled data. This book, therefore, introduces a new unified view on learning with different settings of unlabeled data. This book consists of two parts: the first part analyzes the fundamental assumptions of SSL and proposes a few efficient SSL algorithms; the second part discusses three learning frameworks to deal with other settings of unlabeled data. This book should be helpful to researchers or graduate students in areas with abundance of unlabeled data, such as computer vision, bioinformatics, web mining, and natural language processing.
More details
Language
English
Place of publication
Germany
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 220 mm
Width: 150 mm
Thickness: 9 mm
Weight
215 gr
ISBN-13
978-3-8433-7910-6 (9783843379106)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Zenglin Xu, PhD. He is currently a researcher in Department of Computer Science of Purdue University, US. His research interests include machine learning and its applications to information retrieval, web search and social computing. Irwin King and Michael R. Lyu are professors with the Chinese University of Hong Kong.
Author
The Chinese University of Hong Kong, Shatin
The Chinese University of Hong Kong, Shatin