Text mining with machine learning : principles and techniques / Jan Žižka, František Dařena, Arnošt Svoboda.

Žižka, Jan
Call Number
006.3/12
Author
Žižka, Jan, author.
Title
Text mining with machine learning : principles and techniques / Jan Žižka, František Dařena, Arnošt Svoboda.
Physical Description
1 online resource
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.
Summary
"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"--
Added Author
Dařena, František, 1979- author.
Svoboda, Arnošt, 1949- author.
Subject
MACHINE LEARNING.
COMPUTATIONAL LINGUISTICS.
Semantics Data processing.
COMPUTERS / Database Management / Data Mining
COMPUTERS / Machine Theory
MATHEMATICS / Arithmetic
Multimedia
Total Ratings: 0
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$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.
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No Reviews to Display
Summary
"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"--
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.
Subject
MACHINE LEARNING.
COMPUTATIONAL LINGUISTICS.
Semantics Data processing.
COMPUTERS / Database Management / Data Mining
COMPUTERS / Machine Theory
MATHEMATICS / Arithmetic
Multimedia