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Semi-Supervised Learning with Committees: Exploiting Unlabeled Data using Ensemble Learning Algorithms - Couverture souple
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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- ÉditeurSüdwestdeutscher Verlag für Hochschulschriften
- Date d'édition2011
- ISBN 10 3838125703
- ISBN 13 9783838125701
- ReliureBroché
- Langueanglais
- Nombre de pages304
- Coordonnées du fabricantnon disponible
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Semi-Supervised Learning with Committees
Edité par
Südwestdeutscher Verlag für Hochschulschriften, 2011
ISBN 10 : 3838125703
ISBN 13 : 9783838125701
Neuf
Couverture souple
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Etat : 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 . N° de réf. du vendeur 5406908
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Semi-Supervised Learning with Committees : Exploiting Unlabeled Data using Ensemble Learning Algorithms
Edité par
Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG, 2011
ISBN 10 : 3838125703
ISBN 13 : 9783838125701
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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). N° de réf. du vendeur 9783838125701
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Semi-Supervised Learning with Committees
ISBN 10 : 3838125703
ISBN 13 : 9783838125701
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783838125701
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Semi-Supervised Learning with Committees
ISBN 10 : 3838125703
ISBN 13 : 9783838125701
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783838125701
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