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Text mining with machine learning

  1. Title statementText mining with machine learning : principles and techniques / Jan Žižka, František Dařena, Arnošt Svoboda.
    PublicationBoca Raton, FL : CRC Press, 2020.
    Phys.des.1 online resource
    ISBN0429469276 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 NoteIncludes bibliographical references and index.
    Contents1. 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 AvailabilityPřístup 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
    ConspectCOM - 021030
    COM - 037000
    MAT - 004000
    UN
    CountryFlorika
    Languageangličtina
    Document kindElectronic books
    URLPlný text pro studenty a zaměstnance UPOL
    book

    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.

Number of the records: 1  

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