Machine Learning Theory and Applications [Hardcover]

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.

• very relevant to current research challenges faced in various fields
• self-contained reference to machine learning

• emphasis on applications-oriented techniques

1. The Sequential Bootstrap 2. The Cross-Entropy Method for Estimation 3. The Cross-Entropy Method for Optimization 4. Probability Collectives in Optimization 5. Bagging, Boosting, and Random Forests Using R 6. Matching Score Fusion Methods 7. Statistical Methods on Special Manifolds for Image and Video Understanding 8. Dictionary-based Methods for Object Recognition 9. Conditional Random Fields for Scene Labeling 10. Shape Based Image Classification and Retrieval 11. Visual Search: A Large-Scale Perspective 12. Video Activity Recognition by Luminance Differential Trajectory and Aligned Projection Distance 13. Soft Biometrics for Surveillance: An Overview 14. A User Behavior Monitoring and Profiling Scheme for Masquerade Detection 15. Application of Bayesian Graphical Models to Iris Recognition 16. Learning Algorithms for Document Layout Analysis 17. Hidden Markov Models for Off-Line Cursive Handwriting Recognition 18. Machine LearnlÓq