• Publications
  • Influence
Neuro-Fuzzy Architectures and Hybrid Learning
  • D. Rutkowska
  • Computer Science
  • Studies in Fuzziness and Soft Computing
  • 5 February 2002
TLDR
The main idea of this book is to present novel connectionist archite ctures of neuro-fuzzy systems, especially those based on the logical approach to fuzzy inference. Expand
A New Version of the Fuzzy-ID3 Algorithm
In this paper, a new version of the Fuzzy-ID3 algorithm is presented. The new algorithm allows to construct decision trees with smaller number of nodes. This is because of the modification that manyExpand
Medical Diagnosis with Type-2 Fuzzy Decision Trees
TLDR
In this paper, we propose type-2 fuzzy decision trees in application to medical diagnosis. Expand
Type-2 Fuzzy Decision Trees
TLDR
A modified fuzzy double clustering algorithm is proposed as a method for generating type-2 fuzzy sets. Expand
Face Classification Based on Linguistic Description of Facial Features
TLDR
This paper presents an artificial intelligence approach towards classification of persons based on verbal descriptions of their facial features. Expand
On the Parzen Kernel-Based Probability Density Function Learning Procedures Over Time-Varying Streaming Data With Applications to Pattern Classification
TLDR
In this paper, we propose a recursive variant of the Parzen kernel density estimator (KDE) to track changes of dynamic density over data streams in a nonstationary environment. Expand
An Expert System for Human Personality Characteristics Recognition
TLDR
In this paper, a hybrid expert system that can recognize some personality characteristics, based on human face pictures, is proposed. Expand
On Generating Fuzzy Rules by an Evolutionary Approach
  • D. Rutkowska
  • Mathematics, Computer Science
  • Cybern. Syst.
  • 1 June 1998
TLDR
This paper shows how knowledge, in the form of fuzzy rules, can be extracted from a fuzzy neural network. Expand
Hybrid Learning Methods
TLDR
In Chapters 4 and 5 the connectionist, multi-layer architectures of fuzzy systems, called fuzzy inference neural networks, were presented. Expand
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