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Visual and Non-Visual Analysis in Archaeology
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Author(s): Juan A. Barceló (Universitat Autònoma de Barcelona, Spain)
Copyright: 2009
Pages: 18
Source title:
Computational Intelligence in Archaeology
Source Author(s)/Editor(s): Juan A. Barcelo (Universidad Autonoma de Barcelona, Spain)
DOI: 10.4018/978-1-59904-489-7.ch005
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Abstract
As we have discussed in previous chapters, an artificial neural network is an information-processing system that maps a descriptive feature vector into a class assignment vector. In so doing, a neural network is nothing more than a complex and intrinsically nonlinear statistical classifier. It extracts the statistical central tendency of a series of exemplars (the learning set) and thus comes to encode information not just about the specific exemplars, but about the stereotypical featureset displayed in the training data (Churchland, 1989; Clark, 1989, 1993; Franklin, 1995). That means, it will discover which sets of features are most commonly present in the exemplars, or commonly occurring groupings of features. In this way, semantic features statistically frequent in a set of learning exemplars come to be both highly marked and mutually associated. “Highly marked” means that the connection weights about such common features tend to be quite strong. “Mutually associated” means that co-occurring features are encoded in such a way that the activation of one of them will promote the activation of the other.
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