a neuro-fuzzy graphic object classifier with modified distance measure estimator

A NEURO-FUZZY GRAPHIC OBJECT CLASSIFIER WITH MODIFIED DISTANCE MEASURE ESTIMATOR

The paper analyses issues leading to errors in graphic object classifiers. Thedistance measures suggested in literature and used as a basis in traditional, fuzzy, andNeuro-Fuzzy classifiers are found to be not suitable for classification of non-stylized orfuzzy objects in which the features of classes are much more difficult to recognize becauseof significant uncertainties in their location and...

Revision of a fuzzy distance measure

A Neuro-Fuzzy Classifier and Its Applications

membership in each class. This viewpoint not only reflects the reality of many applications in which categories have fuzzy boundaries, but also Provides a simple representstion of the potentially complex partition of the feature space. In brief, we use fuzzy i fthen rules to describe a ChsSifier. A typical fuzzy classification rule is like: Fuzzy classification is the task of partitioning a fea...

Some properties of a new fuzzy distance measure

revision of a fuzzy distance measure

Accuracy-based Neuro And Neuro-fuzzy Classifier Systems

Learning Classifier Systems traditionally use a binary representation with wildcards added to allow for generalizations over the problem encoding. However, the simple scheme can be limiting in complex domains. In this paper we present results from the use of neural network-based representation schemes within the accuracy-based XCS. Here each rule’s condition and action are represented by a smal...