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Statistical and Neural Classifiers

An Integrated Approach to Design
BuchKartoniert, Paperback
295 Seiten
Englisch
Springererschienen am09.04.20142001
Neural networks have not only provided a variety of novel or supplementary approaches for pattern recognition tasks, but have also offered architectures on which many well-known statistical pattern recognition algorithms can be mapped for efficient (hardware) implementation.mehr
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BuchGebunden
EUR172,50
BuchKartoniert, Paperback
EUR117,69
E-BookPDF1 - PDF WatermarkE-Book
EUR106,99

Produkt

KlappentextNeural networks have not only provided a variety of novel or supplementary approaches for pattern recognition tasks, but have also offered architectures on which many well-known statistical pattern recognition algorithms can be mapped for efficient (hardware) implementation.
Details
ISBN/GTIN978-1-4471-1071-2
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2014
Erscheinungsdatum09.04.2014
Auflage2001
ReiheAdvances in Computer Vision and Pattern Recognition
Seiten295 Seiten
SpracheEnglisch
Gewicht498 g
IllustrationenXXIII, 295 p. 40 illus.
Artikel-Nr.31837329

Inhalt/Kritik

Inhaltsverzeichnis
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.- 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.- A.5 Matlab Codes (the Non-Linear SLP Training, the First Order Tree Dependence Model, and Data Whitening Transformation).- References.mehr