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Machine Learning: Theory and Applications - 1st Edition - ISBN: 9780444538598, 9780444538666

Machine Learning: Theory and Applications, Volume 31

1st Edition

Series Volume Editors: C. R. Rao Venu Govindaraju
Hardcover ISBN: 9780444538598
Paperback ISBN: 9781493302437
eBook ISBN: 9780444538666
Imprint: North Holland
Published Date: 16th May 2013
Page Count: 552
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Table of Contents

Contributors: Vol. 31

Preface to Handbook Volume – 31


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



Chapter 2. The Cross-Entropy Method for Estimation

1 Introduction

2 Estimation setting

3 Extensions



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


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


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


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


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



Chapter 8. Dictionary-Based Methods for Object Recognition∗

1 Introduction

2 Sparse representation

3 Dictionary learning

4 Concluding remarks


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


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


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


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


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


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



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



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


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



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


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



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


Subject Index


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


Computer scientists, artificial intelligence researchers, biometricians, and statisticians


No. of pages:
© North Holland 2013
16th May 2013
North Holland
Hardcover ISBN:
Paperback ISBN:
eBook ISBN:

Ratings and Reviews

About the Series Volume Editors

C. R. Rao

C. R. Rao

Professor C. R. Rao, born in India, is one of this century's foremost statisticians, and received his education in statistics at the Indian Statistical Institute (ISI), Calcutta. He is Emeritus Holder of the Eberly Family Chair in Statistics at Penn State and Director of the Center for Multivariate Analysis. He has long been recognized as one of the world's top statisticians, and has been awarded 34 honorary doctorates from universities in 19 countries spanning 6 continents. His research has influenced not only statistics, but also the physical, social and natural sciences and engineering.

In 2011 he was recipient of the Royal Statistical Society's Guy Medal in Gold which is awarded triennially to those "who are judged to have merited a signal mark of distinction by reason of their innovative contributions to the theory or application of statistics". It can be awarded both to fellows (members) of the Society and to non-fellows. Since its inception 120 years ago the Gold Medal has been awarded to 34 distinguished statisticians. The first medal was awarded to Charles Booth in 1892. Only two statisticians, H. Cramer (Norwegian) and J. Neyman (Polish), outside Great Britain were awarded the Gold medal and C. R. Rao is the first non-European and non-American to receive the award.

Other awards he has received are the Gold Medal of Calcutta University, Wilks Medal of the American Statistical Association, Wilks Army Medal, Guy Medal in Silver of the Royal Statistical Society (UK), Megnadh Saha Medal and Srinivasa Ramanujan Medal of the Indian National Science Academy, J.C.Bose Gold Medal of Bose Institute and Mahalanobis Centenary Gold Medal of the Indian Science Congress, the Bhatnagar award of the Council of Scientific and Industrial Research, India and the Government of India honored him with the second highest civilian award, Padma Vibhushan, for “outstanding contributions to Science and Engineering / Statistics”, and also instituted a cash award in honor of C R Rao, “to be given once in two years to a young statistician for work done during the preceding 3 years in any field of statistics”.

For his outstanding achievements Rao has been honored with the establishment of an institute named after him, C.R.Rao Advanced Institute for Mathematics, Statistics and Computer Science, in the campus of the University of Hyderabad, India.

Affiliations and Expertise

The Pennsylvania State University, University Park, PA, USA

Venu Govindaraju

Venu Govindaraju

Dr. Venu Govindaraju, SUNY Distinguished Professor of Computer Science and Engineering, is the Vice President of Research and Economic Development of the University at Buffalo and founding director of the Center for Unified Biometrics and Sensors. He received his Bachelor’s degree with honors from the Indian Institute of Technology (IIT) in 1986, and his Ph.D. from UB in 1992. His research focus is on machine learning and pattern recognition in the domains of Document Image Analysis and Biometrics. Dr. Govindaraju has co-authored about 400 refereed scientific papers. His seminal work in handwriting recognition was at the core of the first handwritten address interpretation system used by the US Postal Service. He was also the prime technical lead responsible for technology transfer to the Postal Services in US, Australia, and UK. He has been a Principal or Co-Investigator of sponsored projects funded for about 65 million dollars. Dr. Govindaraju has supervised the dissertations of 30 doctoral students. He has served on the editorial boards of premier journals such as the IEEE Transactions on Pattern Analysis and Machine Intelligence and is currently the Editor-in-Chief of the IEEE Biometrics Council Compendium. Dr. Govindaraju is a Fellow of the ACM (Association of Computing Machinery), IEEE (Institute of Electrical and Electronics Engineers), AAAS (American Association for the Advancement of Science), the IAPR (International Association of Pattern Recognition), and the SPIE (International Society of Optics and Photonics). He is recipient of the 2004 MIT Global Indus Technovator award and the 2010 IEEE Technical Achievement award.

Affiliations and Expertise

The State University of New York, Buffalo, NY, USA