More Than Semi-supervised Learning

Zenglin Xu

ISBN 10: 3843379106 / ISBN 13: 9783843379106

Published by LAP LAMBERT Academic Publishing Dez 2010, 2010

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 132 pp. Englisch.

Seller Inventory # 9783843379106

Bibliographic Details

• Title: More Than Semi-supervised Learning
• Publisher: LAP LAMBERT Academic Publishing Dez 2010
• Publication Date: 2010
• Language: English
• ISBN 10: 3843379106
• ISBN 13: 9783843379106
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 215 grams

About this title

Synopsis

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.

About the Author

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.