Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Handbook of Neural Computation
Handbook of Neural Computation
A Rough Set Approach to Multiple Classifier Systems
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks
ECML '07 Proceedings of the 18th European conference on Machine Learning
An analysis of a lymphoma/leukaemia dataset using rough sets and neural networks
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
Attribute selection for EEG signal classification using rough sets and neural networks
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Unsupervised learning of image recognition with neural society for clustering
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
The rough set exploration system
Transactions on Rough Sets III
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This paper presents a new model of an artificial neural network solving classification problems, called Local Transfer Function Classifier (LTF-C). Its architecture is very similar to this of the Radial Basis Function neural network (RBF), however it utilizes an entirely different learning algorithm. This algorithm is composed of four main parts: changing positions of reception fields, changing their sizes, insertion of new hidden neurons and removal of unnecessary ones during the training.The paper presents also results of LTF-C application to three real-life tasks: handwritten digit recognition, credit approval and cancer diagnosis. LTF-C was able to solve each of these problems with better accuracy than most popular classification systems. Moreover, LTF-C was relatively small and fast.