Statistical Applications of Neural Networks
by Sangit Chatterjee and Matthew Laudato.
An elementary introduction to the theory of neutral networks is made from a statistical perspective. The networks are estimated by two different methods - one derivative based and the other derivative free. The theory is illustrated by three different examples and where possible results are compared to those obtained from a classical statistical model. The methodology is seen as a new paradigm for data analysis where models are not explicitly stated but rather implicitly defined by the network.
Back-propagation, Data Analysis, Feed Forward, Genetic Algorithm.
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