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Machine Learning Text by Aggarwal Charu (96 results)
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Machine Learning for Text
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Machine Learning for Text
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Condition: Acceptable. Item in acceptable condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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hardcover. Condition: 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!
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Condition: new.
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Condition: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
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Condition: Good. Exact ISBN match. Immediate shipping. No funny business.
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hardcover. Condition: 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!
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Machine Learning for Text
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
hardcover. Condition: Fine.
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hardcover. Condition: Good. 2nd ed. 2022. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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Hardcover. Condition: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.
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hardcover. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
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Machine Learning for Text
First Edition
Hardcover. Condition: Near Fine. 1st Edition. First edition, first printing. Near Fine- lightly rubbed and bumped hardback issued without dust jacket, clean and unmarked. Heavy oversize book may require a variable shipping surcharge based on its final destination.
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Condition: New.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Condition: As New. Unread book in perfect condition.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Condition: new.
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Condition: New. pp. 520.
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Condition: New.
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Condition: New. pp. 520.
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Condition: New.
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Condition: As New. Unread book in perfect condition.
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Condition: As New. Unread book in perfect condition.
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Machine Learning for Text (Paperback)
Language: English
Published by Springer Nature Switzerland AG, Cham, 2023
ISBN 10: 3030966259 ISBN 13: 9783030966256
Paperback. Condition: new. Paperback. This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Machine Learning for Text (Hardcover)
Language: English
Published by Springer International Publishing AG, Cham, 2018
ISBN 10: 3319735306 ISBN 13: 9783319735306
First Edition
Hardcover. Condition: new. Hardcover. Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories:- Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Condition: New. pp. 520.
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Condition: New. pp. 565.
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Machine Learning for Text
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
