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.

Key Features

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


 

Readership

Computer scientists, artificial intelligence researchers, biometricians, and statisticians

Table of Contents

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 t

Details

No. of pages:
552
Language:
English
Copyright:
© 2013
Published:
Imprint:
North Holland
Electronic ISBN:
9780444538666
Print ISBN:
9780444538598
Print ISBN:
9781493302437