D ata C lassification. Algorithms and Applications

Front Cover
Dedication
Contents
Editor Biography
Contributors
Preface
Chapter 1: An Introduction to Data Classification
Chapter 2: Feature Selection for Classification: A Review
Chapter 3: Probabilistic Models for Classification
Chapter 4: Decision Trees: Theory and Algorithms
Chapter 5: Rule-Based Classification
Chapter 6: Instance-Based Learning: A Survey
Chapter 7: Support Vector Machines
Chapter 8: Neural Networks: A Review
Chapter 9: A Survey of Stream Classification Algorithms
Chapter 10: Big Data Classification
Chapter 11: Text Classification
Chapter 12: Multimedia Classification
Chapter 13: Time Series Data Classification
Chapter 14: Discrete Sequence Classification
Chapter 15: Collective Classification of Network Data
Chapter 16: Uncertain Data Classification
Chapter 17: Rare Class Learning
Chapter 18: Distance Metric Learning for Data Classification
Chapter 19: Ensemble Learning
Chapter 20: Semi-Supervised Learning
Chapter 21: Transfer Learning
Chapter 22: Active Learning: A Survey
Chapter 23: Visual Classification
Chapter 24: Evaluation of Classification Methods
Chapter 25: Educational and Software Resources for Data Classification
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