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 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.
Jan ika 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. Frantiek Däena 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. Arnot 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, Arnot has worked also as a teacher and researcher at Masaryk University in Brno. His current interest are machine learning and data mining.
Inhaltsangabe
Introduction to the Text Mining. Problematics. Textual Data in Natural Languages and Their Computer Representation. Typical Tasks and Problems. Basic Processing Tools. Machine Learning and Its Application. Applying Sequences of Machine Learning Algorithms. R-language and Its Use for Machine Learning-Based Text Mining. Real-World-Data Examples and Their Basic Preprocessing Using R. Advanced Text Mining Using Machine Learning and R. Selecting Appropriate Machine Learning Algorithms. Examples of Typical Task Solutions. Interpretation of Results.
Introduction to the Text Mining. Problematics. Textual Data in Natural Languages and Their Computer Representation. Typical Tasks and Problems. Basic Processing Tools. Machine Learning and Its Application. Applying Sequences of Machine Learning Algorithms. R-language and Its Use for Machine Learning-Based Text Mining. Real-World-Data Examples and Their Basic Preprocessing Using R. Advanced Text Mining Using Machine Learning and R. Selecting Appropriate Machine Learning Algorithms. Examples of Typical Task Solutions. Interpretation of Results.
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