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Statistical and Neural Classifiers An Integrated Approach to Design

Automatic (machine) recognition, description, classification, and groupings of patterns are important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. Given a pattern, its r...

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Bibliographic Details
Main Author: Raudys, Sarunas
Format: eBook
Language:English
Published: London Springer London 2001, 2001
Edition:1st ed. 2001
Series:Advances in Computer Vision and Pattern Recognition
Subjects:
Artificial Intelligence
Automated Pattern Recognition
Pattern Recognition Systems
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • A.5 Matlab Codes (the Non-Linear SLP Training, the First Order Tree Dependence Model, and Data Whitening Transformation)
  • References
  • 3.1 Bayes, Conditional, Expected, and Asymptotic Probabilities of Misclassification
  • 3.2 Generalisation Error of the Euclidean Distance Classifier
  • 3.3 Most Favourable and Least Favourable Distributions of the Data
  • 3.4 Generalisation Errors for Modifications of the Standard Linear Classifier
  • 3.5 Common Parameters in Different Competing Pattern Classes
  • 3.6 Minimum Empirical Error and Maximal Margin Classifiers
  • 3.7 Parzen Window Classifier
  • 3.8 Multinomial Classifier
  • 3.9 Bibliographical and Historical Remarks
  • 4. Neural Network Classifiers
  • 4.1 Training Dynamics of the Single Layer Perceptron
  • 4.2 Non-linear Decision Boundaries
  • 4.3 Training Peculiarities of the Perceptrons
  • 4.4 Generalisation of the Perceptrons
  • 4.5 Overtraining and Initialisation
  • 4.6 Tools to Control Complexity
  • 4.7 TheCo-Operation of the Neural Networks
  • 4.8 Bibliographical and Historical Remarks
  • 5. Integration of Statistical and Neural Approaches
  • 5.1 Statistical Methods or Neural Nets?
  • 5.2 Positive and Negative Attributes of Statistical Pattern Recognition
  • 5.3 Positive and Negative Attributes of Artificial Neural Networks
  • 5.4 Merging Statistical Classifiers and Neural Networks
  • 5.5 Data Transformations for the Integrated Approach
  • 5.6 The Statistical Approach in Multilayer Feed-forward Networks
  • 5.7 Concluding and Bibliographical Remarks
  • 6. Model Selection
  • 6.1 Classification Errors and their Estimation Methods
  • 6.2 Simplified Performance Measures
  • 6.3 Accuracy of Performance Estimates
  • 6.4 Feature Ranking and the Optimal Number of Feature
  • 6.5 The Accuracy of the Model Selection
  • 6.6 Additional Bibliographical Remarks
  • Appendices
  • A.1 Elements of Matrix Algebra
  • A.2 The First Order Tree Type Dependence Model
  • A.3 Temporal Dependence Models
  • A.4 Pikelis Algorithm for Evaluating Means and Variances of the True, Apparent and Ideal Errors in Model Selection
  • 1. Quick Overview
  • 1.1 The Classifier Design Problem
  • 1.2 Single Layer and Multilayer Perceptrons
  • 1.3 The SLP as the Euclidean Distance and the Fisher Linear Classifiers
  • 1.4 The Generalisation Error of the EDC and the Fisher DF
  • 1.5 Optimal Complexity — The Scissors Effect
  • 1.6 Overtraining in Neural Networks
  • 1.7 Bibliographical and Historical Remarks
  • 2. Taxonomy of Pattern Classification Algorithms
  • 2.1 Principles of Statistical Decision Theory
  • 2.2 Four Parametric Statistical Classifiers
  • 2.3 Structures of the Covariance Matrices
  • 2.4 The Bayes Predictive Approach to Design Optimal Classification Rules
  • 2.5. Modifications of the Standard Linear and Quadratic DF
  • 2.6 Nonparametric Local Statistical Classifiers
  • 2.7 Minimum Empirical Error and Maximal Margin Linear Classifiers
  • 2.8 Piecewise-Linear Classifiers
  • 2.9 Classifiers for Categorical Data
  • 2.10 Bibliographical and Historical Remarks
  • 3. Performance and the Generalisation Error

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