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Añadir al carritoHardcover. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.27.
Idioma: Inglés
Publicado por Springer International Publishing AG, Cham, 2013
ISBN 10: 3031010213 ISBN 13: 9783031010217
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 30,51
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Añadir al carritoPaperback. Condición: new. Paperback. This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees ("this algorithm never does too badly") than about useful rules of thumb ("in this case this algorithm may perform really well"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant. This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Librería: California Books, Miami, FL, Estados Unidos de America
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 30,38
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Idioma: Inglés
Publicado por The MIT Press (edition 1), 2010
ISBN 10: 0262514125 ISBN 13: 9780262514125
Librería: BooksRun, Philadelphia, PA, Estados Unidos de America
EUR 36,68
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Añadir al carritoPaperback. Condición: Good. 1. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.
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Añadir al carritopaperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 36,80
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 37,21
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Idioma: Inglés
Publicado por Springer International Publishing AG, Cham, 2014
ISBN 10: 3031004434 ISBN 13: 9783031004438
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 39,50
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Añadir al carritoPaperback. Condición: new. Paperback. While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: BookHolders, Towson, MD, Estados Unidos de America
EUR 36,30
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Añadir al carritoCondición: Very Good. [ No Hassle 30 Day Returns ][ Ships Daily ] [ Underlining/Highlighting: NONE ] [ Writing: NONE ] [ Edition: Reprint ] Publisher: The MIT Press Pub Date: 9/22/2006 Binding: Hardcover Pages: 528 Reprint edition.
Idioma: Inglés
Publicado por Springer International Publishing AG, Cham, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 41,62
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Añadir al carritoPaperback. Condición: new. Paperback. Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines/ Human Semi-Supervised Learning / Theory and Outlook Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: California Books, Miami, FL, Estados Unidos de America
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Grey Matter Books, Hadley, MA, Estados Unidos de America
Miembro de asociación: SNEAB
EUR 39,03
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Añadir al carritoHardcover. Condición: Fine. Estado de la sobrecubierta: Very Good. Fine, minute shelf wear to dust jacket, pages bright and smooth, all around a nice tight used copy.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 40,40
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Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
Idioma: Inglés
Publicado por Springer International Publishing AG, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 40,11
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 30,83
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Añadir al carritoCondición: New. In English.
Idioma: Inglés
Publicado por Springer International Publishing AG, CH, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Original o primera edición
EUR 44,95
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Añadir al carritoPaperback. Condición: New. 1st.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843379106 ISBN 13: 9783843379106
Librería: Zubal-Books, Since 1961, Cleveland, OH, Estados Unidos de America
EUR 43,58
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Añadir al carritoCondición: New. 132 pp., paperback, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 31,99
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Librería: -OnTimeBooks-, Phoenix, AZ, Estados Unidos de America
EUR 50,59
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Añadir al carritoCondición: good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item for full refund. Ships via media mail.
Librería: Chiron Media, Wallingford, Reino Unido
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Librería: RAMÓN PIGNATELLI, Zaragoza, Z, España
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Añadir al carritoEncuadernación de tapa dura. Condición: Muy Bien. 2? Edición.
Librería: RAMÓN PIGNATELLI, Zaragoza, Z, España
EUR 23,13
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Añadir al carritoEncuadernación de tapa dura. Condición: Muy Bien. 2? Edición.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Añadir al carritoCondición: New. In English.