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Predicting Information Retrieval Performance by Losee Robert (16 results)
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Predicting Information Retrieval Performance (Synthesis Lectures on Information Concepts, Retrieval, and Services)
Language: English
Published by Morgan & Claypool Publishers, 2018
ISBN 10: 1681734729 ISBN 13: 9781681734729
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Predicting Information Retrieval Performance (Synthesis Lectures on Information Concepts, Retrieval, and Services)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 33.39
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In English.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Predicting Information Retrieval Performance
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Predicting Information Retrieval Performance
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Predicting Information Retrieval Performance
Language: English
Published by Springer International Publishing, 2018
ISBN 10: 3031011899 ISBN 13: 9783031011894
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively.This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.
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Taschenbuch. Condition: Neu. Predicting Information Retrieval Performance | Robert M. Losee | Taschenbuch | Synthesis Lectures on Information Concepts, Retrieval, and Services | xix | Englisch | 2018 | Springer | EAN 9783031011894 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Predicting Information Retrieval Performance
Language: English
Published by Springer International Publishing Dez 2018, 2018
ISBN 10: 3031011899 ISBN 13: 9783031011894
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively.This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested. 80 pp. Englisch.
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Condition: New. PRINT ON DEMAND.
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Predicting Information Retrieval Performance
Language: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2018
ISBN 10: 3031011899 ISBN 13: 9783031011894
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a meas.
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Predicting Information Retrieval Performance
Language: English
Published by Springer, Palgrave Macmillan Dez 2018, 2018
ISBN 10: 3031011899 ISBN 13: 9783031011894
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively.This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 80 pp. Englisch.
