Fuzzy Classifier Design

• Κύριος συγγραφέας: Kuncheva, Ludmila I. (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Heidelberg : Physica-Verlag HD : Imprint: Physica, 2000.
• Έκδοση: 1st ed. 2000.
• Σειρά: Studies in Fuzziness and Soft Computing, 49
• Θέματα: Pattern recognition.; Mathematical logic.; Data structures (Computer science).; Artificial intelligence.; Information technology.; Business-Data processing.; Pattern Recognition.; Mathematical Logic and Foundations.; Data Structures and Information Theory.; Artificial Intelligence.; IT in Business.

Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever since have been a center of many discussions, fervently admired and condemned. Both proponents and opponents consider the argu­ ments pointless because none of them would step back from their territory. And st...

Πίνακας περιεχομένων:

• 1. Introduction
• 1.1 What are fuzzy classifiers?
• 1.2 The data sets used in this book
• 1.3 Notations and acronyms
• 1.4 Organization of the book
• 1.5 Acknowledgements
• 2. Statistical pattern recognition
• 2.1 Class, feature, feature space
• 2.2 Classifier, discriminant functions, classification regions
• 2.3 Clustering
• 2.4 Prior probabilities, class-conditional probability density functions, posterior probabilities
• 2.5 Minimum error and minimum risk classification. Loss matrix
• 2.6 Performance estimation
• 2.7 Experimental comparison of classifiers
• 2.8 A taxonomy of classifier design methods
• 3. Statistical classifiers
• 3.1 Parametric classifiers
• 3.2 Nonparametric classifiers
• 3.3 Finding k-nn prototypes
• 3.4 Neural networks
• 4. Fuzzy sets
• 4.1 Fuzzy logic, an oxymoron?
• 4.2 Basic definitions
• 4.3 Operations on fuzzy sets
• 4.4 Determining membership functions
• 5. Fuzzy if-then classifiers
• 5.1 Fuzzy if-then systems
• 5.2 Function approximation with fuzzy if-then systems
• 5.3 Fuzzy if-then classifiers
• 5.4 Universal approximation and equivalences of fuzzy if-then classifiers
• 6. Training of fuzzy if-then classifiers
• 6.1 Expert opinion or data analysis?
• 6.2 Tuning the consequents
• 6.3 Toning the antecedents
• 6.4 Tuning antecedents and consequents using clustering
• 6.5 Genetic algorithms for tuning fuzzy if-then classifiers
• 6.6 Fuzzy classifiers and neural networks: hybridization or identity?
• 6.7 Forget interpretability and choose a model
• 7. Non if-then fuzzy models
• 7.1 Early ideas
• 7.2 Fuzzy k-nearest neighbors (k-nn) designs
• 7.3 Generalized nearest prototype classifier (GNPC)
• 8. Combinations of multiple classifiers using fuzzy sets
• 8.1 Combining classifiers: the variety of paradigms
• 8.2 Classifier selection
• 8.3 Classifier fusion
• 8.4 Experimental results
• 9. Conclusions: What to choose?
• A. Appendix: Numerical results
• A.1 Cone-torus data
• A.2 Normal mixtures data.
• A.3 Phoneme data
• A.4 Satimage data
• References.