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Semi-Supervised Learning with Committees: Exploiting Unlabeled Data using Ensemble Learning Algorithms - Softcover
Synopsis
Supervised learning is a branch of artificial intelligence concerned with developing computer programs that automatically improve with experience through knowledge extraction from examples. Such learning approaches are particularly useful for tasks involving the automatic categorization, retrieval and extraction of knowledge from large collections of data such as text, images and videos. It builds predictive models from labeled data. However, labeling the training data is difficult, expensive, or time consuming, as it requires the effort of human annotators sometimes with specific domain experience. Semi-supervised learning (SSL) aims to minimize the cost of manual annotation by allowing the model to exploit part or all of the available unlabeled data. Semi-supervised learning and ensemble learning are two different paradigms that were developed almost in parallel. Semi-supervised learning tries to improve generalization performance by exploiting unlabeled data, while ensemble learning tries to achieve the same objective by constructing multiple predictors. This book concentrates on SSL with ensembles(committees).
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About the Author
He received his doctoral degree in 2011 from the University of Ulm in Germany thanks to a scholarship offered by the the German Academic Exchange Service (DAAD). From 2001 to 2006, he was a researcher at IBM Cairo's "Technology Development Center". His research interests include machine learning, data mining, and pattern recognition.
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- PublisherSüdwestdeutscher Verlag für Hochschulschriften
- Publication date2011
- ISBN 10 3838125703
- ISBN 13 9783838125701
- BindingPaperback
- LanguageEnglish
- Number of pages304
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Semi-Supervised Learning with Committees
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Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG Jun 2015, 2015
ISBN 10: 3838125703
ISBN 13: 9783838125701
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Supervised learning is a branch of artificial intelligence concerned with developing computer programs that automatically improve with experience through knowledge extraction from examples. Such learning approaches are particularly useful for tasks involving the automatic categorization, retrieval and extraction of knowledge from large collections of data such as text, images and videos. It builds predictive models from labeled data. However, labeling the training data is difficult, expensive, or time consuming, as it requires the effort of human annotators sometimes with specific domain experience. Semi-supervised learning (SSL) aims to minimize the cost of manual annotation by allowing the model to exploit part or all of the available unlabeled data. Semi-supervised learning and ensemble learning are two different paradigms that were developed almost in parallel. Semi-supervised learning tries to improve generalization performance by exploiting unlabeled data, while ensemble learning tries to achieve the same objective by constructing multiple predictors. This book concentrates on SSL with ensembles (committees). 304 pp. Englisch. Seller Inventory # 9783838125701
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Semi-Supervised Learning with Committees
Published by
Südwestdeutscher Verlag für Hochschulschriften, 2011
ISBN 10: 3838125703
ISBN 13: 9783838125701
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abdel Hady Mohamed FaroukHe received his doctoral degree in 2011 from the University of Ulm in Germany thanks to a scholarship offered by the the German Academic Exchange Service (DAAD). From 2001 to 2006, he was a researcher at IBM . Seller Inventory # 5406908
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Semi-Supervised Learning with Committees : Exploiting Unlabeled Data using Ensemble Learning Algorithms
Published by
Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG, 2011
ISBN 10: 3838125703
ISBN 13: 9783838125701
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Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Supervised learning is a branch of artificial intelligence concerned with developing computer programs that automatically improve with experience through knowledge extraction from examples. Such learning approaches are particularly useful for tasks involving the automatic categorization, retrieval and extraction of knowledge from large collections of data such as text, images and videos. It builds predictive models from labeled data. However, labeling the training data is difficult, expensive, or time consuming, as it requires the effort of human annotators sometimes with specific domain experience. Semi-supervised learning (SSL) aims to minimize the cost of manual annotation by allowing the model to exploit part or all of the available unlabeled data. Semi-supervised learning and ensemble learning are two different paradigms that were developed almost in parallel. Semi-supervised learning tries to improve generalization performance by exploiting unlabeled data, while ensemble learning tries to achieve the same objective by constructing multiple predictors. This book concentrates on SSL with ensembles (committees). Seller Inventory # 9783838125701
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Semi-Supervised Learning with Committees
Published by
Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG Jun 2015, 2015
ISBN 10: 3838125703
ISBN 13: 9783838125701
New
Taschenbuch
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Taschenbuch. Condition: Neu. Neuware -Supervised learning is a branch of artificial intelligence concerned with developing computer programs that automatically improve with experience through knowledge extraction from examples. Such learning approaches are particularly useful for tasks involving the automatic categorization, retrieval and extraction of knowledge from large collections of data such as text, images and videos. It builds predictive models from labeled data. However, labeling the training data is difficult, expensive, or time consuming, as it requires the effort of human annotators sometimes with specific domain experience. Semi-supervised learning (SSL) aims to minimize the cost of manual annotation by allowing the model to exploit part or all of the available unlabeled data. Semi-supervised learning and ensemble learning are two different paradigms that were developed almost in parallel. Semi-supervised learning tries to improve generalization performance by exploiting unlabeled data, while ensemble learning tries to achieve the same objective by constructing multiple predictors. This book concentrates on SSL with ensembles (committees).Books on Demand GmbH, Überseering 33, 22297 Hamburg 304 pp. Englisch. Seller Inventory # 9783838125701
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