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Sinopsis
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.
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Reseña del editor
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.
Biografía del autor
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.
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More Than Semi-supervised Learning: A unified view on Learning with Labeled and Unlabeled Data
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LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106
ISBN 13: 9783843379106
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More Than Semisupervised Learning A unified view on Learning with Labeled and Unlabeled Data
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LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106
ISBN 13: 9783843379106
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More Than Semisupervised Learning A unified view on Learning with Labeled and Unlabeled Data
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ISBN 10: 3843379106
ISBN 13: 9783843379106
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More Than Semi-supervised Learning: A unified view on Learning with Labeled and Unlabeled Data
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LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106
ISBN 13: 9783843379106
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More Than Semi-supervised Learning
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LAP LAMBERT Academic Publishing, 2010
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ISBN 13: 9783843379106
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Xu ZenglinZenglin 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 . Nº de ref. del artículo: 5467773
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More Than Semi-supervised Learning
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LAP LAMBERT Academic Publishing Dez 2010, 2010
ISBN 10: 3843379106
ISBN 13: 9783843379106
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Taschenbuch. Condición: Neu. 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. Nº de ref. del artículo: 9783843379106
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More Than Semi-supervised Learning: A unified view on Learning with Labeled and Unlabeled Data
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LAP LAMBERT Academic Publishing 2010-12-28, 2010
ISBN 10: 3843379106
ISBN 13: 9783843379106
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More Than Semi-supervised Learning : A unified view on Learning with Labeled and Unlabeled Data
Publicado por
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106
ISBN 13: 9783843379106
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783843379106
Cantidad disponible: 1 disponibles
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More Than Semi-supervised Learning: A unified view on Learning with Labeled and Unlabeled Data
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LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106
ISBN 13: 9783843379106
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More Than Semi-supervised Learning: A unified view on Learning with Labeled and Unlabeled Data
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LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106
ISBN 13: 9783843379106
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