9783031010279 - learning to rank for information retrieval and natural language processing, second edition (synthesis lectures on human language technologies) de li, hang (15 resultados)
- Tapa blanda
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 37,90
Envío por EUR 2,29Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 40,61
Envío por EUR 2,29Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
- Tapa blanda
Librería: Basi6 International, Irving, TX, Estados Unidos de AmericaBasi6 International
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 47,82
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 10 disponibles
Condición: Brand New. New. Delivery takes 25-30 days. Excellent Customer Service.
- Tapa blanda
Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 38,51
Envío por EUR 14,03Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. In English.
- Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 37,44
Envío por EUR 17,56Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 54,07
Envío por EUR 3,46Se envía dentro de Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. 2nd edition NO-PA16APR2015-KAP.
- Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 43,75
Envío por EUR 17,56Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
- Tapa blanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 38,97
Envío por EUR 61,24Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: 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 cond…ucted 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.
- Tapa blanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 35,15
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition | Hang Li | Taschenbuch | Synthesis Lectures on Human Language Technologies | xiii | Englisch | 2014 | Springer | EAN 9783031010279 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17,…69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Tapa blanda
- Impresión bajo demanda
Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 32,62
Envío por EUR 5,50Se envía de Italia a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: new. Questo è un articolo print on demand.
- Tapa blanda
- Impresión bajo demanda
Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 51,99
Envío por EUR 7,61Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand.
- Tapa blanda
- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 35,30
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: 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 studie…s 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.
- Tapa blanda
- Impresión bajo demanda
Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 53,38
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. PRINT ON DEMAND.
Idioma: Inglés
Editorial: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2014
- Tapa blanda
- Impresión bajo demanda
Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 32,69
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: 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. I…ntensive studies have been conducted on .
- Tapa blanda
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 35,30
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 ha…ve 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 WorkSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 124 pp. Englisch.
