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We consider a new neural network for data discrimination in pattern recognition applications. We refer to this as a maximum discriminating feature (MDF) neural network. Its weights are obtained in closed-form, thereby overcoming problems associated with other nonlinear neural networks. It uses neuron activation functions that are dynamically chosen based on(More)
We address both recognition of true classes and rejection of unseen false classes inputs, as occurs in many realistic pattern recognition problems. We advance a hierarchical binary-decision classifier and produce analog outputs at each node, with values proportional to the class conditional probabilities. This yields a new soft-decision hierarchical(More)