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Published by Morgan & Claypool, 2011
ISBN 10: 1608457079ISBN 13: 9781608457076
Seller: suffolkbooks, Center moriches, NY, U.S.A.
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Published by Morgan & Claypool, 2011
ISBN 10: 1608457079ISBN 13: 9781608457076
Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.
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Paperback. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 0.46.
Published by Morgan & Claypool Publishers, 2011
ISBN 10: 1608457079ISBN 13: 9781608457076
Book First Edition
Paperback. Condition: Very Good. First Edition. Binding firm, cover has shelf wear, interior clean and unmarked. Top corner curled. First Edition.
Published by Springer, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Published by Springer, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
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Published by Springer, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Published by Springer 11/5/2014, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
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Paperback or Softback. Condition: New. Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition 0.5. Book.
Published by Springer, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
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Published by Springer, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
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Published by Springer, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
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Published by Morgan & Claypool Publishers, 2014
ISBN 10: 1627055843ISBN 13: 9781627055840
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Published by Springer, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
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Published by Springer International Publishing Nov 2014, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Book Print on Demand
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work 124 pp. Englisch.
Published by Springer, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
Book
Condition: As New. Unread book in perfect condition.
Published by Springer International Publishing, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work.
Published by Morgan & Claypool Publishers, 2011
ISBN 10: 1608457079ISBN 13: 9781608457076
Seller: Studibuch, Stuttgart, Germany
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paperback. Condition: Gut. 114 Seiten; 9781608457076.3 Sprache: Deutsch Gewicht in Gramm: 500.
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2014
ISBN 10: 3031010272ISBN 13: 9783031010279
Seller: moluna, Greven, Germany
Book Print on Demand
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on .
Published by Morgan & Claypool Publishers, 2011
ISBN 10: 1608457079ISBN 13: 9781608457076
Seller: dsmbooks, Liverpool, United Kingdom
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paperback. Condition: Good. Good. book.