Download e-book for iPad: Artificial Neural Networks: Methods and Applications by David J. Livingstone
By David J. Livingstone
ISBN-10: 1588297187
ISBN-13: 9781588297181
ISBN-10: 1603271015
ISBN-13: 9781603271011
During this booklet, overseas specialists file the heritage of the appliance of ANN to chemical and organic difficulties, supply a advisor to community architectures, education and the extraction of ideas from knowledgeable networks, and canopy many state of the art examples of the appliance of ANN to chemistry and biology. equipment regarding the mapping and interpretation of Infra pink spectra and modelling environmental toxicology are incorporated. This publication is a superb advisor to this interesting box.
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Extra resources for Artificial Neural Networks: Methods and Applications (Methods in Molecular Biology, Vol. 458)
Example text
Of course, one can expect that the quality of predicted (simulated) infrared spectra depends on the coverage of the structural domain by the training data. 2. 1. Kohonen Neural Networks Kohonen neural networks were initially developed with the aim to mimic human brain functioning, mainly the storage of information and memory. In human brains, similar information is stored in certain regions (neighboring neurons) of the cortex. This is related to the mapping of inputs in the Kohonen map. The unsupervised nature of learning strategy of the Kohonen neural networks is rationalized by the way young children learn to recognize objects.
Of course, one can expect that the quality of predicted (simulated) infrared spectra depends on the coverage of the structural domain by the training data. 2. 1. Kohonen Neural Networks Kohonen neural networks were initially developed with the aim to mimic human brain functioning, mainly the storage of information and memory. In human brains, similar information is stored in certain regions (neighboring neurons) of the cortex. This is related to the mapping of inputs in the Kohonen map. The unsupervised nature of learning strategy of the Kohonen neural networks is rationalized by the way young children learn to recognize objects.
Polley MJ, Burden FR, Winkler, DA. (2005) Predictive human intestinal absorption QSAR models using Bayesian regularized neural networks. Aust J Chem 58:859–863. 12. Burden F R (1996) Using artificial neural networks to predict biological activity from simple molecular structure considerations. Quant Struct-Act Relat 15:7–11. 13. Burden FR (1989) Molecular identification number for substructure searches. J Chem Inf Comput Sci 29:225–227. 14. Winkler DA, Burden FR (2004) Bayesian neural nets for modeling in drug discovery.
Artificial Neural Networks: Methods and Applications (Methods in Molecular Biology, Vol. 458) by David J. Livingstone
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