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Handbook of Statistics

Book 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.



  • very relevant to current research challenges faced in various fields
  • self-contained reference to machine learning
  • emphasis on applications-oriented techniques

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Contributors: Vol. 31
  6. Preface to Handbook Volume – 31
  7. Introduction
    1. 1 Part I—Theoretical aspects
    2. 2 Part II—Object recognition
    3. 3 Part III—Biometric systems
    4. 4 Part IV—Document analysis
  8. Part I: Theoretical Analysis
    1. Chapter 1. The Sequential Bootstrap
      1. 1 Introduction
      2. 2 A sequential bootstrap resampling scheme
      3. 3 Bootstrapping empirical measures with a random sample size
      4. 4 Convergence rates for the sequential bootstrap
      5. 5 Second-order correctness of the sequential bootstrap
      6. 6 Concluding remarks
      7. Acknowledgments
      8. References
    2. Chapter 2. The Cross-Entropy Method for Estimation
      1. 1 Introduction
      2. 2 Estimation setting
      3. 3 Extensions
      4. Acknowledgement
      5. References
    3. Chapter 3. The Cross-Entropy Method for Optimization
      1. 1 Introduction
      2. 2 From estimation to optimization
      3. 3 Applications to combinatorial optimization
      4. 4 Continuous optimization
      5. 5 Summary
      6. References
    4. Chapter 4. Probability Collectives in Optimization
      1. 1 Introduction
      2. 2 Delayed sampling theory
      3. 3 Delayed sampling experiments
      4. 4 Immediate sampling theory
      5. 5 Immediate sampling experiments
      6. 6 Conclusion
      7. References
    5. Chapter 5. Bagging, Boosting, and Random Forests Using R
      1. 1 Introduction
      2. 2 Data sets and rationale
      3. 3 Bagging
      4. 4 Boosting
      5. 5 Do Bagging and Boosting really work?
      6. 6 What is a classification tree?
      7. 7 Classification tree versus logistic regression
      8. 8 Random forest
      9. 9 Random forest, genetics, and cross-validation
      10. 10 Regression trees
      11. 11 Boosting using the R package, ada
      12. 12 Epilog
      13. References
    6. Chapter 6. Matching Score Fusion Methods
      1. 1 Introduction
      2. 2 Matching systems
      3. 3 Selected approaches to fusion in matching systems
      4. 4 Operating modes of matching systems
      5. 5 Complexity types of classifier combination methods
      6. 6 Modeling matching score dependencies
      7. 7 Score combination applications
      8. 8 Conclusion
      9. References
  9. Part II: Object Recognition
    1. Chapter 7. Statistical Methods on Special Manifolds for Image and Video Understanding
      1. 1 Introduction
      2. 2 Some motivating examples
      3. 3 Differential geometric tools
      4. 4 Common manifolds arising in image analysis
      5. 5 Applications in image analysis
      6. 6 Summary and discussion
      7. Acknowledgments
      8. References
    2. Chapter 8. Dictionary-Based Methods for Object Recognition∗
      1. 1 Introduction
      2. 2 Sparse representation
      3. 3 Dictionary learning
      4. 4 Concluding remarks
      5. References
    3. Chapter 9. Conditional Random Fields for Scene Labeling
      1. 1 Introduction
      2. 2 Overview of CRF
      3. 3 Scene parsing
      4. 4 More recent implementations of CRF scene labelings
      5. 5 Conclusion and future directions
      6. References
    4. Chapter 10. Shape-Based Image Classification and Retrieval
      1. 1 Introduction
      2. 2 Prior work
      3. 3 Classification and retrieval models
      4. 4 Features
      5. 5 Classification experiments
      6. 6 Retrieval
      7. 7 Multiple class labels
      8. 8 Summary and conclusions
      9. References
    5. Chapter 11. Visual Search: A Large-Scale Perspective
      1. 1 Introduction
      2. 2 When is big data important?
      3. 3 Information extraction and representation
      4. 4 Matching images
      5. 5 Practical considerations: memory footprint and speed
      6. 6 Benchmark data sets
      7. 7 Closing remarks
      8. References
  10. Part III: Biometric Systems
    1. Chapter 12. Video Activity Recognition by Luminance Differential Trajectory and Aligned Projection Distance
      1. 1 Introduction
      2. 2 Related work
      3. 3 Problem formulation
      4. 4 DLFT and LAPD solutions
      5. 5 Experiments
      6. 6 Conclusion
      7. References
    2. Chapter 13. Soft Biometrics for Surveillance: An Overview
      1. 1 Introduction
      2. 2 Performance metrics
      3. 3 Incorporating soft biometrics in a fusion framework
      4. 4 Human identification using soft biometrics
      5. 5 Predicting gender from face images
      6. 6 Applications
      7. 7 Conclusion
      8. References
    3. Chapter 14. A User Behavior Monitoring and Profiling Scheme for Masquerade Detection
      1. 1 Introduction
      2. 2 Related work
      3. 3 Support Vector Machines (SVMs)
      4. 4 Data collection, feature extraction, and feature vector generation
      5. 5 Experimental design
      6. 6 Discussion and conclusion
      7. Acknowledgments
      8. References
    4. Chapter 15. Application of Bayesian Graphical Models to Iris Recognition
      1. 1 Introduction
      2. 2 Gabor wavelet-based matching
      3. 3 Correlation filter-based iris matching
      4. 4 Bayesian graphical model for iris recognition
      5. 5 Summary
      6. Acknowledgments
      7. References
  11. Part IV: Document Analysis
    1. Chapter 16. Learning Algorithms for Document Layout Analysis
      1. 1 Introduction
      2. 2 Pixel classification
      3. 3 Zone classification
      4. 4 Connected component classification
      5. 5 Text region segmentation
      6. 6 Region classification
      7. 7 Functional labeling
      8. 8 Conclusion
      9. References
    2. Chapter 17. Hidden Markov Models for Off-Line Cursive Handwriting Recognition
      1. 1 Introduction
      2. 2 Serialization of handwriting images
      3. 3 HMM-based text line recognition
      4. 4 Outlook and conclusions
      5. Acknowledgment
      6. References
    3. Chapter 18. Machine Learning in Handwritten Arabic Text Recognition
      1. 1 Introduction
      2. 2 Arabic script—challenges for recognition
      3. 3 Learning paradigms
      4. 4 Features for text recognition
      5. 5 Models for recognition
      6. 6 Conclusion
      7. References
    4. Chapter 19. Manifold Learning for the Shape-Based Recognition of Historical Arabic Documents
      1. 1 Introduction
      2. 2 Problem statement
      3. 3 Manifold learning
      4. 4 Feature extraction
      5. 5 Experimental results
      6. 6 Conclusion and future prospects
      7. Acknowledgments
      8. References
    5. Chapter 20. Query Suggestion with Large Scale Data
      1. 1 Introduction
      2. 2 Terminology
      3. 3 Approaches to generation of Query Suggestions
      4. 4 Evaluation methods of QS
      5. 5 Properties of large scale data
      6. 6 Query Suggestion in practice
      7. 7 Closing remarks
      8. References
  12. Subject Index