Feature selection and creation are two of the most important and difficult tasks in the field of pattern classification. Good features improve the performance of both conventional and neural network pattern classifiers. Exemplar selection is another task that can reduce the memory and computation requirements of a KNN classifier.

This work addresses three problems with reinforcement learning and adaptive neuro-control: 1. Non-Markovian interfaces between learner and environment.

ABSTRACT A large number of VLSI implementationsof neural networkmodels have been reported. The diversityof these implementations is noteworthy. This paper attempts to put a group of representative VLSI implementations in perspective by comparing and contrasting them. IMPLEMENTATION Changing the way information is represented can be beneficial. For examplea change of representation can make information more compact for storage and transmission.

Previous work has shown the ability of Multilayer Perceptrons (MLPs) to estimate emission probabilities for Hidden Markov Models The advantages of a speech recognition system incorporating(HMMs).

We have used a neural network to compute corrections for images written by electron beams to eliminate the proximity effects caused by electron scattering.

We are concerned with the problem of the number of nodes needed in a feedforward neural network in order to represent a fUllction to within a specified accuracy.

Harmonic grammar (Legendre, et al., 1990) is a connectionist theory of linguistic on the assumption that the well-formednesswell-formed ness based of a sentence can be measured by the harmony (negative energy) of the corresponding connectionist state. Assuming a lower-level connectionist that obeys a few general connectionist principles but is otherwisenetwork we construct a higher-level network with an equivalent harmonyunspecified, function that captures the most linguistically relevant global aspects of the lower level network. In this paper, we extend the tensor product representation (Smolensky 1990) to fully recursive representations of recursively structured objects like sentences in the lower-level network.

Simple classical spin models well-known to physicists as the ANNNI and Heisenberg XY Models. in which long-range interactions occur in a pattern given by the Mexican Hat operator.

The signal content of the codes encoded by a presynaptic neuron will be decoded by some other neurons postsynpatically. Neurons are often thought to be encoding a single type of 282 Signal Processing by Multiplexing and Demultiplexing in Neurons 283 codes. But there is evidence suggesting that neurons may encode more than one type of signals. One of the mechanisms for embedding multiple types of signals processed by a neuron is multiplexing. When the signals are multiplexed, they also need to be demultiplexed to extract the useful information transmitted by the neurons. Theoretical and experimental evidence of such multiplexing and demultiplexing scheme for signal processing by neurons will be given below.

Although color TV is an established technology, there are a number of longstanding problems for which neural networks may be suited. Impulse noise is such a problem, and a modular neural network approach is presented inthis paper. The training and analysis was done on conventional computers, while real-time simulations were performed on a massively parallel computercalled the Princeton Engine. The network approach was compared to a conventional alternative, a median filter. Real-time simulations andquantitative analysis demonstrated the technical superiority of the neural system. Ongoing work is investigating the complexity and cost of implementing this system in hardware.