The cascade-correlation learning architecture
Advances in neural information processing systems 2
Neural networks and the bias/variance dilemma
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Neural Processing Letters
Training methods for adaptive boosting of neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
Neural Computation
Modelling a Query Space Using Associations
Proceedings of the 2011 conference on Information Modelling and Knowledge Bases XXII
A fast classification algorithm based on local models
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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An associative neural network (ASNN) is a combination of an ensemble of the feed-forward neural networks and the K-nearest neighbor technique. The introduced network uses correlation between ensemble responses as a measure of distance among the analyzed cases for the nearest neighbor technique and provides an improved prediction by the bias correction of the neural network ensemble both for function approximation and classification. Actually, the proposed method corrects a bias of a global model for a considered data case by analyzing the biases of its nearest neighbors determined in the space of calculated models. An associative neural network has a memory that can coincide with the training set. If new data become available the network can provide a reasonable approximation of such data without a need to retrain the neural network ensemble. Applications of ASNN for prediction of lipophilicity of chemical compounds and classification of UCI letter and satellite data set are presented. The developed algorithm is available on-line at http://www.virtuallaboratory.org/lab/asnn.