Text Mining with Machine Learning (PDF)
Principles and Techniques
(Sprache: Englisch)
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...
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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.
Autoren-Porträt von Jan Zizka, Frantisek Darena, Arnost Svoboda
Jan Zizka is a consultant in machine learning and data mining. He has worked as a system programmer, developer of advanced software systems, and researcher. For the last 25 years, he has devoted himself to AI and machine learning, especially text mining. He has been a faculty at a number of universities and research institutes. He has authored approximately 100 international publications.FrantiSek Darena is an associate professor and the head of the Text Mining and NLP group at the Department of Informatics, Mendel University, Brno. He has published numerous articles in international scientific journals, conference proceedings, and monographs, and is a member of editorial boards of several international journals. His research includes text/data mining, intelligent data processing, and machine learning.
ArnoSt Svoboda is an expert programer. His speciality includes programming languages and systems such as R, Assembler, Matlab, PL/1, Cobol, Fortran, Pascal, and others. He started as a system programmer. The last 20 years, ArnoSt has worked also as a teacher and researcher at Masaryk University in Brno. His current interest are machine learning and data mining.
Bibliographische Angaben
- Autoren: Jan Zizka , Frantisek Darena , Arnost Svoboda
- 2019, 1. Auflage, 366 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 0429890273
- ISBN-13: 9780429890277
- Erscheinungsdatum: 31.10.2019
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 7.64 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
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