Handbook of Statistics book cover

Handbook of Statistics

Machine Learning: Theory and Applications

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.

Computer scientists, artificial intelligence researchers, biometricians, and statisticians

Included in series
Handbook of Statistics

Hardbound, 552 Pages

Published: May 2013

Imprint: North-holland

ISBN: 978-0-444-53859-8


  • Part I - Theoretical Aspects
    chapter 1 - The Sequential Bootstrap (P.K. Pathak and C.R. Rao)
    chapter 2 - The Cross-Entropy Method for Estimation (Dirk P. Kroese, Reuven Y. Rubinstein and Peter W. Glynn)
    chapter 3 - The Cross-Entropy Method for Optimization (Zdravko I. Botev, Dirk P. Kroese, Reuven Y. Rubinstein and Pierre L’Ecuyer)
    chapter 4 - Probability Collectives in Optimization (David H. Wolpert, Stefan R. Bieniawski and Dev G. Rajnarayan)
    chapter 5 - Bagging, Boosting, and Random Forests Using R (Hansen Bannerman-Thompson, M. Bhaskara Rao and Subramanyam Kasala)
    chapter 6 - Matching Score Fusion Methods (Sergey Tulyakov and Venu Govindaraju)

    Part II - Object Recognition
    chapter 7 - Statistical Methods on Special Manifolds for Image and Video Understanding (Pavan Turaga, Rama Chellappa and Anuj Srivastava)
    chapter 8 - Dictionary-based Methods for Object Recognition (Vishal M. Patel and Rama Chellappa)
    chapter 9 - Conditional Random Fields for Scene Labeling (Ifeoma Nwogu and Venu Govindaraju)
    chapter 10 - Shape Based Image Classification and Retrieval (N. Mohanty, A. Lee-St. John, R. Manmatha and T. M. Rath)
    chapter 11 - Visual Search: A Large-Scale Perspective (Robinson Piramuthu, Anurag Bhardwaj, Wei Di and Neel Sundaresan)

    Part III - Biometric Systems
    chapter 12 - Video Activity Recognition by Luminance Differential Trajectory and Aligned Projection Distance (Haomian Zheng, Zhu Li, Yun Fu, Aggelos K. Katsaggelos and Jane You)
    chapter 13 - Soft Biometrics for Surveillance: An Overview (D. A. Reid, S. Samangooei, C. Chen, M. S. Nixon and A. Ross)
    chapter 14 - A User Behavior Monitoring and Profiling Scheme for Masquerade Detection (Ashish Garg, Shambhu Upadhyaya)
    chapter 15 - Application of Bayesian Graphical Models to Iris Recognition (B.V.K. Vijaya Kumar, Vishnu Naresh Boddeti, Jon Smereka, Jason Thornton and Marios Savvides)

    Part IV - Document Analysis
    chapter 16 - Learning Algorithms for Document Layout Analysis (Simone Marinai)
    chapter 17 - Hidden Markov Models for Off-Line Cursive Handwriting Recognition (Andreas Fischer, Volkmar Frinken and Horst Bunke)
    chapter 18 - Machine Learning in Handwritten Arabic Text Recognition (Utkarsh Porwal, Zhixin Shi and Srirangaraj Setlur)
    chapter 19 - Manifold learning for the shape-based recognition of historical Arabic documents (Mohamed Cheriet, Reza Farrahi Moghaddam and Ehsan Arabnejad)
    chapter 20 - Query Suggestion with Large Scale Data (Nish Parikh, Gyanit Singh and Neel Sundaresan)


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