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Full Description
Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field.The Handbook is divided in two sections: Theory and Applications, covering machine learning, data analytics, biometrics, document recognition and security.
Contents
1. The Sequential Bootstrap2. The Cross-Entropy Method for Estimation3. The Cross-Entropy Method for Optimization4. Probability Collectives in Optimization5. Bagging, Boosting, and Random Forests Using R6. Matching Score Fusion Methods7. Statistical Methods on Special Manifolds for Image and Video Understanding8. Dictionary-based Methods for Object Recognition9. Conditional Random Fields for Scene Labeling10. Shape Based Image Classification and Retrieval11. Visual Search: A Large-Scale Perspective12. Video Activity Recognition by Luminance Differential Trajectory and Aligned Projection Distance13. Soft Biometrics for Surveillance: An Overview14. A User Behavior Monitoring and Profiling Scheme for Masquerade Detection 15. Application of Bayesian Graphical Models to Iris Recognition16. Learning Algorithms for Document Layout Analysis17. Hidden Markov Models for Off-Line Cursive Handwriting Recognition18. Machine Learning in Handwritten Arabic Text Recognition19. Manifold learning for the shape-based recognition of historical Arabic documents20. Query Suggestion with Large Scale Data
