Virtual Computational Chemistry Laboratory

Welcome to the Associative Neural Network (ASNN)

ASNN represents a combination of an ensemble of feed-forward neural networks and the k-nearest neighbour technique. This method uses the correlation between ensemble responses as a measure of distance amid the analysed cases for the nearest neighbour technique. This provides an improved prediction by the bias correction of the neural network ensemble. An associative neural network has a memory that can coincide with the training set. If new data becomes available, the network further improves its predictive ability and provides a reasonable approximation of the unknown function without a need to retrain the neural network ensemble. This feature of the method dramatically improves its predictive ability over traditional neural networks and k-nearest neighbour techniques. Another important feature of ASNN is the possibility to interpret neural network results by analysis of correlations between data cases in the space of models.

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3. Tetko, I. V.; Tanchuk, V. Y. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program, J. Chem. Inf. Comput. Sci., 2002, 42, 1136-45, article.
   
   
4. Tetko, I. V. Associative Neural Network, CogPrints Archive, cog00001441, 2001, article.
   
   

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