Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more. 
Home Multivariate Data Modeling Neural Networks Mapping of Spaces  
See also: Introduction to ANNs  
ANN  Mapping of Spaces
Newcomers in the field of multivariate data analysis and neural networks often think that an artificial neural network (ANN) is a type of a black magic box which can be used to enter data and get a solution back. Although this view is potentially dangerous, there is also a grain of truth in it. We could therefore regard an ANN as an abstract machine which creates a nonlinear mapping between an ndimensional input data space and a pdimension output space. n is usually much larger than p; with p often being in the range of 1 to 3 (since the human interpreter is restricted to a maximum dimensionality of 3 or perhaps 4). This nonlinear mapping is set up during the learning process of a neural network. The "art" of training a neural network is to control the training in such a way that the resulting mapping represents the underlying relationship within the data, avoiding any adjustment for noise, or errors in the data.


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Last Update: 20121008