Isbn: 9783319735306 - machine learning for text (9 Ergebnisse)

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Zustand: Good. Good condition. A copy that has been read but remains intact. May contain markings such as bookplates, stamps, limited notes and highlighting, or a few light stains.

Hardcover. Zustand: Very Good. 2018 ed. Minimal wear to cover. Pages clean and binding tight. shelf wear. bumped edges. Hardcover.

Zustand: 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.

xxiii, 493 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Zustand: New. In English.

Hardcover. Zustand: Brand New. 493 pages. 10.00x7.00x1.25 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbookcarefully covers a coherently organized framework drawn from these intersectingtopics. The chapters of this textbook is organized into three categories:- Basic algorithms: Chapters 1 through 7 discuss the classical algorithmsfor machine learning from text such as preprocessing, similaritycomputation, topic modeling, matrix factorization, clustering,classification, regression, and ensemble analysis.- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methodsfrom text when combined with different domains such as multimedia andthe Web. The problem of information retrieval and Web search is alsodiscussed in the context of its relationship with ranking and machinelearning methods.- Sequence-centric mining: Chapters 10 through 14 discuss varioussequence-centric and natural language applications, such as featureengineering, neural language models, deep learning, text summarization,information extraction, opinion mining, text segmentation, and eventdetection.This textbook covers machine learning topics for text in detail. Since thecoverage 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 offercourses not just in text analytics but also from the broader perspective ofmachine learning (with text as a backdrop).This textbook targets graduate students in computer science, as well as researchers, professors, and industrialpractitioners working in these related fields. This textbook is accompanied with a solution manual forclassroom teaching.…