We develop algorithms for use with a recently developed VLSI architecture for pattern classification. This architecture is based on the evaluation of class discriminant functions without crossterms. We refer to classifiers that use such discriminant functions as Sigma classifiers. Tradeoffs in architecture design have been required to allow high throughput and consequently the decision regions in feature space which can be generated using this architecture form a restricted subset of possible decision regions. We investigate the properties of such decision regions and associated classifier training algorithms.

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