Natural language information retrieval (175 resultados)

Condición: Good. A sound copy with only light wear. Overall a solid copy at a great price.

Hardcover. Condición: As New. AS NEW hardcover, no marks in text, very clean exterior, appears unread. Book.

Condición: New. pp. ix + 406 Illus.

Hardcover. Condición: Very Good. This book is in very good condition; no remainder marks. Appears to have been gently used. Inside pages are clean. ; 6.25 X 0.5 X 9.25 inches; 202 pages.

Soft cover. Condición: New. ISBN:9780195692327,Territorial restriction maybe printed on the book. This is an Int'l edition, ISBN and cover may differ from US edition, Contents same as US edition.

paperback. Condición: Very Good. Legacy smart filter userdata3=general (pricing criteria).

Condición: As New. Unread book in perfect condition.

Condición: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Condición: New. Brand New, Softcover edition. This item may ship from the US or our Overseas warehouse depending on your location and stock availability.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Gr.-8°, hardcover. Condición: Sehr gut. 409 Seiten : Ausgetragenes Bibliotheksexemplar, Buch ist in einem sehr guten Zustand, Schnitt gestempelt, Papier in sehr gutem Zustand. B12-03-06G Sprache: Englisch Gewicht in Gramm: 1647.

Condición: New.

Hardcover. Condición: new. New Copy. Customer Service Guaranteed.

Condición: New. In English.

hardcover. Condición: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.

Condición: As New. Unread book in perfect condition.

Condición: New. 2nd edition NO-PA16APR2015-KAP.

Original decorated boards. Condición: Very Good. Reprint. With diagrams. Light wear to spine, covers & corners.; 8vo; 192 pages.

Condición: As New. Unread book in perfect condition.

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 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. …

Condición: New.

Condición: New.

Condición: New. In English.

Condición: New. In English.

Condición: New.

Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condición: As New. Unread book in perfect condition.

Condición: New.