Supervised and unsupervised pattern recognition : feature extraction and computational intelligence

Bibliographische Detailangaben

Titel
Supervised and unsupervised pattern recognition feature extraction and computational intelligence
verantwortlich
Micheli-Tzanakou, Evangelia
veröffentlicht
[Place of publication not identified]: CRC Press, 2017
Erscheinungsjahr
2017
Teil von
Industrial electronics series.
Medientyp
E-Book
Datenquelle
British Library Catalogue
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Inhaltsangabe:
  • Overviews of Neural Networks, Classifiers, and Feature Extraction Methods -- Supervised Neural Networks -- \ Criteria for Optimal Classifier Design -- \ Categorizing the Classifiers -- \ Bayesian Optimal Classifiers -- \ Exemplar Classifiers -- \ Space Partition Methods -- \ Neural Networks -- \ Bayesian Classifiers -- \ Minimum ECM Classifers -- \ Multi-Class Optimal Classifiers -- \ Bayesian Classifiers with Multivariate Normal Populations -- \ Quadratic Discriminant Score -- \ Linear Discriminant Score -- \ Linear Discriminant Analysis and Classification -- \ Equivalence of LDF to Minimum TPM Classifier -- \ Learning Vector Quantizer (LVQ) -- \ Competitive Learning -- \ Self-Organizing Map -- \ Learning Vector Quantization -- \ Nearest Neighbor Rule -- \ Neural Networks (NN) -- \ Artificial Neural Networks -- \ Usage of Neural Networks -- \ Other Neural Networks -- \ Feed-Forward Neural Networks -- \ Error Backpropagation -- \ Madaline Rule III for Multilayer Network with Sigmoid Function -- \ A Comment on the Terminology 'Backpropagation' -- \ Optimization Machines with Feed-Forward Multilayer Perceptrons -- \ Justification for Gradient Methods for Nonlinear Function Approximation -- \ Training Methods for Feed-Forward Networks -- \ Issues in Neural Networks -- \ Universal Approximation -- \ Enhancing Convergence Rate and Generalization of an Optimization Machine -- \ Suggestions for Improving the Convergence -- \ Quick Prop -- \ Kullback-Leibler Distance -- \ Weight Decay -- \ Regression Methods for Classification Purposes -- \ Two-Group Regression and Linear Discriminant Function.