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More Than Semi-supervised Learning - Xu, Zenglin; King, Irwin; R. Lyu, Michael
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Xu, Zenglin; King, Irwin; R. Lyu, Michael:

More Than Semi-supervised Learning - Taschenbuch

2010, ISBN: 3843379106, Lieferbar binnen 4-6 Wochen Versandkosten:Versandkostenfrei innerhalb der BRD

ID: 9783843379106

Internationaler Buchtitel. In englischer Sprache. Verlag: LAP Lambert Acad. Publ. Paperback, 132 Seiten, L=220mm, B=152mm, H=13mm, Gew.=210gr, [GR: 16390 - HC/Informatik/EDV/Sonstiges], Kartoniert/Broschiert, Klappentext: 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. 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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More Than Semi-supervised Learning - Xu, Zenglin / King, Irwin / R. Lyu, Michael
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Xu, Zenglin / King, Irwin / R. Lyu, Michael:

More Than Semi-supervised Learning - Taschenbuch

2010, ISBN: 3843379106

Gebundene Ausgabe, ID: 10032299

A unified view on Learning with Labeled and Unlabeled Data - Buch, gebundene Ausgabe, 132 S., Beilagen: Paperback, Erschienen: 2010 LAP Lambert Acad. Publ.

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More Than Semi-supervised Learning - Zenglin Xu; Irwin King; Michael R. Lyu
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More Than Semi-supervised Learning - neues Buch

2010

ISBN: 9783843379106

ID: 18161994

A unified view on Learning with Labeled and Unlabeled Data, unbekannt, Buch, [PU: LAP Lambert Academic Publishing]

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More Than Semi-supervised Learning - Zenglin Xu
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Zenglin Xu:
More Than Semi-supervised Learning - Taschenbuch

ISBN: 9783843379106

Paperback, [PU: Lap Lambert Academic Publishing AG & Co Kg], Computing: General

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Details zum Buch
More Than Semi-supervised Learning
Autor:

Xu, Zenglin; King, Irwin; R. Lyu, Michael

Titel:

More Than Semi-supervised Learning

ISBN-Nummer:

9783843379106

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.

Detailangaben zum Buch - More Than Semi-supervised Learning


EAN (ISBN-13): 9783843379106
ISBN (ISBN-10): 3843379106
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: LAP Lambert Acad. Publ.
132 Seiten
Gewicht: 0,210 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 31.03.2009 22:26:44
Buch zuletzt gefunden am 21.10.2015 17:05:01
ISBN/EAN: 9783843379106

ISBN - alternative Schreibweisen:
3-8433-7910-6, 978-3-8433-7910-6

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