Abstract
Assume we have a difficult pattern recognition problem which can easily be handled by a human but not by a machine. Assume also that the human recognition process is difficult to articulate or express in any functional or algorithmic form. Examples of such tasks are face recognition and speaker verification. In some problems we have some knowledge about the classes. An example is handwriting recognition where the theoretical shapes, connections, loops, etc. for each symbol are known, so the “ideal” prototype for each class is described by a set of rules. Nevertheless, handwriting recognition by a machine (and sometimes by a human) is still a challenge. Two natural approaches to designing a classifier are
-
Ask an expert how they solve the problem and try to encapsulate the knowledge in a fuzzy rule-base classifier.
-
Collect input-output data (i.e., a labeled data set) and extract the classifier parameters from the data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kuncheva, L.I. (2000). Training of fuzzy if-then classifiers. In: Fuzzy Classifier Design. Studies in Fuzziness and Soft Computing, vol 49. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1850-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1850-5_6
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2472-8
Online ISBN: 978-3-7908-1850-5
eBook Packages: Springer Book Archive