9783843379106 - more than semi-supervised learning: a unified view on learning with labeled and unlabeled data de xu, zenglin; king, irwin; r. lyu, michael (11 resultados)
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Taschenbuch. Condición: Neu. More Than Semi-supervised Learning | A unified view on Learning with Labeled and Unlabeled Data | Zenglin Xu (u. a.) | Taschenbuch | 132 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843379106 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Sa…arbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.
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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 tradition…al 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.
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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 re…trieval, web search and social .
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 S…SL 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch.
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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 SS…L 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.
