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Text mining with machine learning
Title statement Text mining with machine learning : principles and techniques / Jan Žižka, František Dařena, Arnošt Svoboda. Publication Boca Raton, FL : CRC Press, 2020. Phys.des. 1 online resource ISBN 0429469276 online book 9780429890260 online book 0429890265 online book 9780429890253 (online bk. : Mobipocket) 0429890257 (online bk. : Mobipocket) 9780429890277 (online bk. : PDF) 0429890273 (online bk. : PDF) 9780429469275 (online bk.) Internal Bibliographies/Indexes Note Includes bibliographical references and index. Contents 1. Introduction to Text Mining with Machine Learning -- 2. Introduction to R -- 3. Structured Text Representations -- 4. Classification -- 5. Bayes Classifier -- 6. Nearest Neighbors -- 7. Decision Trees -- 8. Random Forest -- 9. Adaboost -- 10. Support Vector Machines -- 11. Deep Learning -- 12. Clustering -- 13. Word Embeddings -- 14. Feature Selection. Notes to Availability Přístup pouze pro oprávněné uživatele Another responsib. Dařena, František, 1979- Svoboda, Arnošt, 1949- Subj. Headings Machine learning. * Computational linguistics. * Semantics - Data processing. * COMPUTERS / Database Management / Data Mining * COMPUTERS / Machine Theory * MATHEMATICS / Arithmetic * Computational linguistics. * Machine learning. * Semantics - Data processing. Form, Genre elektronické knihy electronic books Conspect COM - 021030 COM - 037000 MAT - 004000 UN Country Florika Language angličtina Document kind Electronic books URL Plný text pro studenty a zaměstnance UPOL book
"This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions, which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc"--
1. Introduction to Text Mining with Machine Learning -- 2. Introduction to R -- 3. Structured Text Representations -- 4. Classification -- 5. Bayes Classifier -- 6. Nearest Neighbors -- 7. Decision Trees -- 8. Random Forest -- 9. Adaboost -- 10. Support Vector Machines -- 11. Deep Learning -- 12. Clustering -- 13. Word Embeddings -- 14. Feature Selection.
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