Machine Learning: Theory and Applications

Contributors: Vol. 31

Preface to Handbook Volume – 31

Introduction

1 Part I—Theoretical aspects

2 Part II—Object recognition

3 Part III—Biometric systems

4 Part IV—Document analysis

Part I: Theoretical Analysis

Chapter 1. The Sequential Bootstrap

1 Introduction

2 A sequential bootstrap resampling scheme

3 Bootstrapping empirical measures with a random sample size

4 Convergence rates for the sequential bootstrap

5 Second-order correctness of the sequential bootstrap

6 Concluding remarks

Acknowledgments

References

Chapter 2. The Cross-Entropy Method for Estimation

1 Introduction

2 Estimation setting

3 Extensions

Acknowledgement

References

Chapter 3. The Cross-Entropy Method for Optimization

1 Introduction

2 From estimation to optimization

3 Applications to combinatorial optimization

4 Continuous optimization

5 Summary

References

Chapter 4. Probability Collectives in Optimization

1 Introduction

2 Delayed sampling theory

3 Delayed sampling experiments

4 Immediate sampling theory

5 Immediate sampling experiments

6 Conclusion

References

Chapter 5. Bagging, Boosting, and Random Forests Using R

1 Introduction

2 Data sets and rationale

3 Bagging

4 Boosting

5 Do Bagging and Boosting really work?

6 What is a classification tree?

7 Classification tree versus logistic regression

8 Random forest

9 Random forest, genetics, and cross-validation

10 Regression trees

11 Boosting using the R package, ada

12 Epilog

References

Chapter 6. Matching Score Fusion Methods

1 Introduction

2 Matching systems

3 Selected approaches to fusion in matching systems

4 Operating modes of matching systems

5 Complexity types of classifier combination methods

6 Modeling matching score dependencies

7 Score combination applications

8 Conclusion

References

Part II: Object Recognition

Chapter 7. Statistical Methods on Special Manifolds for Image and Video Understanding

1 Introduction

2 Some motivating examples

3 Differential geometric tools

4 Common manifolds arising in image analysis

5 Applications in image analysis

6 Summary and discussion

Acknowledgments

References

Chapter 8. Dictionary-Based Methods for Object Recognition∗

1 Introduction

2 Sparse representation

3 Dictionary learning

4 Concluding remarks

References

Chapter 9. Conditional Random Fields for Scene Labeling

1 Introduction

2 Overview of CRF

3 Scene parsing

4 More recent implementations of CRF scene labelings

5 Conclusion and future directions

References

Chapter 10. Shape-Based Image Classification and Retrieval

1 Introduction

2 Prior work

3 Classification and retrieval models

4 Features

5 Classification experiments

6 Retrieval

7 Multiple class labels

8 Summary and conclusions

References

Chapter 11. Visual Search: A Large-Scale Perspective

1 Introduction

2 When is big data important?

3 Information extraction and representation

4 Matching images

5 Practical considerations: memory footprint and speed

6 Benchmark data sets

7 Closing remarks

References

Part III: Biometric Systems

Chapter 12. Video Activity Recognition by Luminance Differential Trajectory and Aligned Projection Distance

1 Introduction

2 Related work

3 Problem formulation

4 DLFT and LAPD solutions

5 Experiments

6 Conclusion

References

Chapter 13. Soft Biometrics for Surveillance: An Overview

1 Introduction

2 Performance metrics

3 Incorporating soft biometrics in a fusion framework

4 Human identification using soft biometrics

5 Predicting gender from face images

6 Applications

7 Conclusion

References

Chapter 14. A User Behavior Monitoring and Profiling Scheme for Masquerade Detection

1 Introduction

2 Related work

3 Support Vector Machines (SVMs)

4 Data collection, feature extraction, and feature vector generation

5 Experimental design

6 Discussion and conclusion

Acknowledgments

References

Chapter 15. Application of Bayesian Graphical Models to Iris Recognition

1 Introduction

2 Gabor wavelet-based matching

3 Correlation filter-based iris matching

4 Bayesian graphical model for iris recognition

5 Summary

Acknowledgments

References

Part IV: Document Analysis

Chapter 16. Learning Algorithms for Document Layout Analysis

1 Introduction

2 Pixel classification

3 Zone classification

4 Connected component classification

5 Text region segmentation

6 Region classification

7 Functional labeling

8 Conclusion

References

Chapter 17. Hidden Markov Models for Off-Line Cursive Handwriting Recognition

1 Introduction

2 Serialization of handwriting images

3 HMM-based text line recognition

4 Outlook and conclusions

Acknowledgment

References

Chapter 18. Machine Learning in Handwritten Arabic Text Recognition

1 Introduction

2 Arabic script—challenges for recognition

3 Learning paradigms

4 Features for text recognition

5 Models for recognition

6 Conclusion

References

Chapter 19. Manifold Learning for the Shape-Based Recognition of Historical Arabic Documents

1 Introduction

2 Problem statement

3 Manifold learning

4 Feature extraction

5 Experimental results

6 Conclusion and future prospects

Acknowledgments

References

Chapter 20. Query Suggestion with Large Scale Data

1 Introduction

2 Terminology

3 Approaches to generation of Query Suggestions

4 Evaluation methods of QS

5 Properties of large scale data

6 Query Suggestion in practice

7 Closing remarks

References

Subject Index