Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering focuses on fuzzy clustering methods which have proven to be very powerful in pattern recognition and considers the entire process of dynamic pattern recognition. This book sets a general framework for Dynamic Pattern Recognition, describing in detail the monitoring process using fuzzy tools and the adaptation process in which the classifiers have to be adapted, using the observations of the dynamic process. It then focuses on the problem of a changing cluster structure (new clusters, merging of clusters, splitting of clusters and the detection of gradual changes in the cluster structure). Finally, the book integrates these parts into a complete algorithm for dynamic fuzzy classifier design and classification.
Rezensionen / Stimmen
`The book could be considered as a lucid, clear and well to read theoretical treatise about fuzzy dynamic pattern recognition. It is particularly commendable that Dr. Angstenberger does not stop here, but that she shows very clearly how her suggestions really work in applications.
In my view this book really presents a breakthrough in a new area of pattern recognition. It will certainly be the basis for many other research projects and real applications. I can only congratulate her to this excellent scientific and application oriented work and hope that it may be of benefit to many scientists and practitioners.'
From the foreword by H.-J. Zimmermann, Aachen, Germany, January 2001
Reihe
Auflage
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Research
Illustrationen
Maße
Höhe: 241 mm
Breite: 160 mm
Dicke: 22 mm
Gewicht
ISBN-13
978-0-7923-7503-6 (9780792375036)
DOI
10.1007/978-94-017-1312-2
Schweitzer Klassifikation
1 Introduction.- 2 General Framework of Dynamic Pattern Recognition.- 3 Stages of the Dynamic Pattern Recognition Process.- 4 Dynamic Fuzzy Classifier Design with Point-Prototype Based Clustering Algorithms.- 5 Similarity Concepts for Dynamic Objects in Pattern Recognition.- 6 Applications of Dynamic Pattern Recognition Methods.- 7 Conclusions.- References.- Unsupervised Optimal Fuzzy Clustering Algorithm of Gath and Geva.- Description of Implemented Software.
