Text mining with machine learning : principles and techniques /: principles and techniques. (2019)

• Record Type: Book
• Further Information: Note: Jan Žižka, František Dařena, Arnošt Svoboda.
• Authors: Žižka, Jan; Dařena, František, 1979-; Svoboda, Arnošt, 1949-
• Contents: Preface Introduction to Text Mining with Machine Learning ; Introduction; Relation of Text Mining to Data Mining; The Text Mining Process; Machine Learning for Text Mining; Three Fundamental Learning Directions; Big Data; About This Book Introduction to R ; Installing R; Running R; RStudio; Writing and Executing Commands; Variables and Data Types; Objects in R; Functions; Operators; Vectors; Matrices and Arrays; Lists; Factors; Data Frames; Functions Useful in Machine Learning; Flow Control Structures; Packages; Graphics Structured text representations; Introduction; The Bag-of-words Model; The Limitations of the Bag-of-Words Model; Document Features; Standardization; Texts in Different Encodings; Language Identification; Tokenization; Sentence Detection; Filtering Stop Words, Common, and Rare Terms; Removing Diacritics; Normalization; Annotation; Calculating the Weights in the Bag-of-Words Model; Common Formats for Storing Structured Data; A Complex Example Classification; Sample Data; Selected Algorithms; Classifier Quality Measurement Bayes Classifier ; Introduction; Bayes’ Theorem; Optimal Bayes Classifier; Na¨ıve Bayes Classifier; Illustrative Example of Na¨ıve Bayes; Na¨ıve Bayes Classifier in R Nearest Neighbors ; Introduction; Similarity as Distance; Illustrative Example of k-NN; k-NN in R Decision Trees; Introduction; Entropy Minimization-Based c5 Algorithm; C5 Tree Generator in R Random Forest; Introduction; Random Forest in R Adaboost ; Introduction; Boosting Principle; Adaboost Principle; Weak Learners; Adaboost in R Support Vector Machines; Introduction; Support Vector Machines Principles; SVM in R Deep Learning ; Introduction; Artificial Neural Networks; Deep Learning in R Clustering ; Introduction to Clustering; Difficulties of Clustering; Similarity Measures; Types of Clustering Algorithms; Clustering Criterion Functions; Deciding on the Number of Clusters; K-means; K-medoids; Criterion Function Optimization; Agglomerative Hierarchical Clustering; Scatter-Gather Algorithm; Divisive Hierarchical Clustering; Constrained Clustering; Evaluating Clustering Results; Cluster Labeling; A Few Examples Word Embeddings ; Introduction; Determining the Context and Word Similarity; Context Windows; Computing Word Embeddings; Aggregation of Word Vectors; An Example Feature Selection ; Introduction; Feature Selection as State Space Search; Feature Selection Methods; Term Elimination Based on Frequency; Term Strength; Term Contribution; Entropy-based Ranking; Term Variance; An Example References Index
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2019
• Extent: 1 online resource, illustrations (black and white, and colour)
• Subjects: 006.312; Machine learning; Computational linguistics; Semantics -- Data processing
• Languages: English
• ISBNs: 9780429890260; 9780429890277; 9780429890253; 9780429469275
• Related ISBNs: 9781138601826
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.