Fuzzy Classifier Design / Edition 1

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This book about fuzzy classifier design briefly introduces the fundamentals of supervised pattern recognition and fuzzy set theory. Fuzzy if-then classifiers are defined and some theoretical properties thereof are studied. Popular training algorithms are detailed. Non if-then fuzzy classifiers include relational, k-nearest neighbor, prototype-based designs, etc. A chapter on multiple classifier combination discusses fuzzy and non-fuzzy models for fusion and selection.

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Product Details

  • ISBN-13: 9783790812985
  • Publisher: Physica-Verlag HD
  • Publication date: 5/19/2000
  • Series: Studies in Fuzziness and Soft Computing Series , #49
  • Edition description: 2000
  • Edition number: 1
  • Pages: 315
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.75 (d)

Table of Contents

Introduction: What are fuzzy classifiers?- The data sets used in this book.- Notations and acronyms.- Organization of the book.- Acknowledgements.- Statistical Pattern Recognition: Class, feature, feature space.- Classifier, discriminant functions, classification regions.- Clustering.- Prior probabilities, class-conditional probability density functions, posterior probabilities.- Minimum error and minimum risk classification. Loss matrix.- Performance estimation.- Experimental comparison of classifiers.- A taxonomy of classifier design methods.- Statistical Classifiers: Parametric classifiers.- Nonparametric classifiers.- Finding k-nn prototypes.- Neural networks.- Fuzzy Sets: Fuzzy logic, an oxymoron?- Basic definitions.- Operations on fuzzy sets.- Determining membership functions.- Fuzzy If-then Classifiers: Fuzzy if-then systems.- Function approximation with fuzzy if-then systems.- Fuzzy if-then classifiers.- Universal approximation and equivalences of fuzzy if-then classifiers.- Training of Fuzzy If-then Classifiers: Expert opinion or data analysis.- Tuning the consequents.- Tuning the antecedents.- Tuning antecedents and consequents using clustering.- Genetic algorithms for tuning fuzzy if-then classifiers.- Fuzzy classifiers and neural networks: hybridization or identity?- Forget interpretability and choose a model.- Non if-then Fuzzy Models: Early ideas.- Fuzzy k-nearest neighbors (k-nn) designs.- Generalized nearest prototype classifier (GNPC).- Combinations of Multiple Classifiers Using Fuzzy Sets: Combining classifiers: the variety of paradigms.- Classifier Selection.- Classifier Fusion.- Experimental results.- Conclusions: What to Choose.

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