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12 April 2004 Categorizing decision strategies through limbic system models
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The solution of difficult optimization problems often requires the use of a parameter set allowing critical algorithm design choices to be set. For example, in the construction of a valid pattern recognition scheme using a simple feed forward network (FFN) technique, there can be thousands of equally valid FFN solutions which achieve high percentage recognition levels on reasonable inputs. The solutions arise from different choices of stopping tolerance, internal neuron architecture, learning rates and so forth. These meta level optimization parameter choices can be used to organize collections of optimization algorithms into matrices W. Each column of the matrix corresponds to a set of parameter choices such a stopping tolerance, learning rate, random restart choices and so forth. For example, an optimization algorithm is constructed from a 4 x 3 matrix W by choosing an entry from each column to construct a sequence ABC. The sequence ABC then encodes the collection of meta parameters that are used to shape the algorithm. In this example, there are thus 64 possible optimization algorithms all chosen to produce a similar output such as recognition rate. A simplified biologically based model of information processing includes primary sensory processing and sensor fusion with construction of higher level meta data modeled via recurrent connections between the site of sensor fusion and a simple model of limbic processing. We illustrate how such a model can be constructed using as training data the matrices described above. Finally, the use of this model to model the decision process is discussed.
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James K. Peterson "Categorizing decision strategies through limbic system models", Proc. SPIE 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004, (12 April 2004);

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