Associative Neural Network


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

DOI: 10.1023/A:1019903710291

Extracted Key Phrases

2 Figures and Tables

Citations per Year

153 Citations

Semantic Scholar estimates that this publication has 153 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Tetko2002AssociativeNN, title={Associative Neural Network}, author={Igor V. Tetko}, journal={Neural Processing Letters}, year={2002}, volume={16}, pages={187-199} }