| 書目名稱 | Fundamentals of Predictive Text Mining | | 編輯 | Sholom M. Weiss,Nitin Indurkhya,Tong Zhang | | 視頻video | http://file.papertrans.cn/351/350484/350484.mp4 | | 概述 | Presents a comprehensive, practical and easy-to-read introduction to text mining.Includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapte | | 叢書名稱 | Texts in Computer Science | | 圖書封面 |  | | 描述 | One consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining – the process of analyzing unstructured natural-language text – is concerned with how to extract information from these documents.Developed from the authors’ highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field.Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining.In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential.Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers.Topics and features: presents a comprehensive, practical and easy-to | | 出版日期 | Textbook 20101st edition | | 關(guān)鍵詞 | Active Learning; Document Classification and Correction; Extraction; Retrieval; Summarization; classifica | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-84996-226-1 | | isbn_softcover | 978-1-4471-2565-5 | | isbn_ebook | 978-1-84996-226-1Series ISSN 1868-0941 Series E-ISSN 1868-095X | | issn_series | 1868-0941 | | copyright | Springer-Verlag London Limited 2010 |
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