A Neural-Network-Based Approach to Optical Symbol Recognition
Neural Processing Letters
Fuzzy classification trees for data analysis
Fuzzy Sets and Systems
Word recognition system using neural networks
Highly parallel computaions
Intelligent data analysis
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
A user-centred corporate acquisition system: a dynamic fuzzy membership functions approach
Decision Support Systems
Fuzzy model based recognition of handwritten numerals
Pattern Recognition
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
Presents a technique to produce fuzzy rules based on the ID3 approach and to optimize defuzzification parameters by using a two-layer perceptron. The technique overcomes the difficulties in a conventional syntactic approach to handwritten character recognition, including problems of choosing a starting or reference point, scaling, and learning by machines. The authors' technique provides: a way to produce meaningful and simple fuzzy rules; a method to fuzzify ID3-derived rules to deal with uncertain, noisy, or fuzzy data; and a framework to incorporate fuzzy rules learned from the training data and those extracted from human recognition experience. The authors' experimental results on NIST Special Database 3 show that the technique out-performs the straight forward ID3 approach. Moreover, ID3-derived fuzzy rules can be combined with an optimized nearest neighbor classifier, which uses intensity features only, to achieve a better classification performance than either of the classifiers. The combined classifier achieves a correct classification rate of 98.6% on the test set