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Classification by balanced binary representation
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AI
DOI：10.1016/0925-2312(96)00047-1.png)
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Abstract
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Classifiers for binary and for real-valued data, consisting of a single internal layer of spherical threshold cells are defined by two fundamental requirements: asymptotic linear separability of the internal representations, which defines the cells' activation threshold, and input-space covering, which defines the minimal number of cells required. Both parameters depend solely on the input dimension. Class assignments are learnt by applying Rosenblatt's learning rule to the internal representations which, by choice of the activation threshold, are balanced, having equally probable bit values, Balancing guarantees that the asymptotic separation capacity of the proposed classifiers is equal to the size of the internal layer. Generalization is achieved when the data points are clustered. The advantage of balancing is demonstrated by application to two 'real-world' problems, one involving binary data and the other real-valued data.
Keywords:
classification
pattern recognition
balancing
learning capacity
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6.5
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6.5W
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