Text Mining with Machine Learning

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 Naive Bayes Classifier Illustrative Example of Naive Bayes Naive 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

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• Algoritmos e estruturas de dados
• Aprendizagem mecânica
• Mineração de Dados