Rodney Winter

TL;DR: The adaptive linear combiner (ALC) as mentioned in this paper was proposed for signal processing and pattern recognition, and practical applications of the ALC in signal processing were described. But it was not used for pattern recognition.

TL;DR: The adaptive linear combiner is described, and practical applications of the ALC in signal processing and pattern recognition are presented, and Adaptive pattern recognition using neural nets is discussed.

TL;DR: A pattern recognition concept involving first an 'invariance net' and second a 'trainable classifier' is proposed, which is expected that the same basic approach will be effective for speech recognition, where insensitivity to certain aspects of speech signals and at the same time sensitivity to other aspects ofspeech signals will be required.

TL;DR: An approximation is derived which expresses the probability of error for an output neuron of a large network (a network with many neurons per layer) as a function of the percentage change in the weights.

TL;DR: A novel algorithm for training multilayer fully connected feedforward networks of ADALINE neurons has been developed, called MRII for MADALINE RULE II, and Architectures that take advantage of MRII's quick learning to produce useful generalizations are presented.