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010 a| 2019035868;

020 a| 9781138601826q| (hardback);

035 a| (WaSeSS)ssj0002227063;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| Q325.5b| .Z59 2019;

082 00 a| 006.3/122| 23;

100 1 a| ¿ưi¿�ka, Jan.?| UNAUTHORIZED;

245 10 a| Text mining with machine learningh| [electronic resource] :b| principles and techniques /c| Jan ¿ưi¿�ka, Machine Learning Consultant, Brono, Czech Republic, Franti¿Łek Da¿™ena, Department of Informatics, Mendel University, Brno, Czech Republic, Arno¿Łt Svoboda, Department of Applied Mathematics & Computer Science, Masaryk University, Brno, Czech Republic.;

250 a| First.;

260 a| Boca Raton :b| CRC Press,c| 2019.;

300 a| xii, 351 pages :b| illustrations (some color) ;c| 25 cm;

504 a| Includes bibliographical references and index.;

505 0 a| 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 -- References -- Index -- Color Section.;

506 a| Available only to authorized users.;

520 a| "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"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Computational linguistics.=| ^A92497;

650 0 a| Semanticsx| Data processing.=| ^A86224;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Da¿™ena, Franti¿Łek,d| 1979-?| UNAUTHORIZED;

700 1 a| Svoboda, Arno¿Łt,d| 1949-?| UNAUTHORIZED;

856 40 z| Full text available from Taylor & Francis eBooksu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.taylorfrancis.com%2Fbooks%2F9780429469275;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

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949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

596 a| 1 3 4;

998 a| 6689462;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6689462-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 12/19/2025x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6689462-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 12/19/2025x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6689462-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 12/19/2025x| EBOOKz| JERESOURCE;

Text mining with machine learning principles and techniques / Jan ¿ưi¿�ka, Machine Learning Consultant, Brono, Czech Republic, Franti¿Łek Da¿™ena, Department of Informatics, Mendel University, Brno, Czech Republic, Arno¿Łt Svoboda, Department of Applied Mathematics & Computer Science, Masaryk University, Brno, Czech Republic.

Author/creator	¿ưi¿�ka, Jan
Other author	Da¿™ena, Franti¿Łek, 1979-
Other author	Svoboda, Arno¿Łt, 1949-
Format	Electronic
Edition	First.
Publication Info	Boca Raton : CRC Press, 2019.
Description	xii, 351 pages : illustrations (some color) ; 25 cm
Supplemental Content	Full text available from Taylor & Francis eBooks
Subjects	Machine learning. Computational linguistics. Semantics--Data processing.

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 -- References -- Index -- Color Section.
Abstract	"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"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2019035868
ISBN	9781138601826 (hardback)

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