eBook Handbook of Statistics, 1st Edition

  • Published By:
  • ISBN-10: 0444538666
  • ISBN-13: 9780444538666
  • DDC: 006.31
  • Grade Level Range: College Freshman - College Senior
  • 552 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013
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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.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Other Frontmatter.
Copyright Page.
Table of Contents.
Other Frontmatter.
Preface to Handbook Volume – 31.
1: Theoretical Analysis.
2: The Sequential Bootstrap.
3: The Cross-Entropy Method for Estimation.
4: The Cross-Entropy Method for Optimization.
5: Probability Collectives in Optimization.
6: Bagging, Boosting, and Random Forests Using R.
7: Matching Score Fusion Methods.
8: Object Recognition.
9: Statistical Methods on Special Manifolds for Image and Video Understanding.
10: Dictionary-Based Methods for Object Recognition∗.
11: Conditional Random Fields for Scene Labeling.
12: Shape-Based Image Classification and Retrieval.
13: Visual Search: A Large-Scale Perspective.
14: Biometric Systems.
15: Video Activity Recognition by Luminance Differential Trajectory and Aligned Projection Distance.
16: Soft Biometrics for Surveillance: An Overview.
17: A User Behavior Monitoring and Profiling Scheme for Masquerade Detection.
18: Application of Bayesian Graphical Models to Iris Recognition.
19: Document Analysis.
20: Learning Algorithms for Document Layout Analysis.
21: Hidden Markov Models for Off-Line Cursive Handwriting Recognition.
22: Machine Learning in Handwritten Arabic Text Recognition.
23: Manifold Learning for the Shape-Based Recognition of Historical Arabic Documents.
24: Query Suggestion with Large Scale Data.
Subject Index.