Quality of the is judged subjectivelyby how much the pseudo-colorresultresult resemblesthe true color image, by RMS quantizationerror, and by run time.

This work presents an Attractor Neural Network (ANN) model of Recall andRecognition. It is shown that an ANN model can qualitatively account for a wide range of experimental psychological data pertaining to the these two main aspects of memory access. Certain psychological phenomena are accounted for, including the effects of list-length, wordfrequency, presentationtime, context shift, and aging. Thereafter, the probabilities of successful Recall and Recognition are estimated, in order to possibly enable further quantitative examination of the model. 1 Motivation The goal of this paper is to demonstrate that a Hopfield-based [Hop82] ANN model can qualitatively account for a wide range of experimental psychological data pertaining tothe two main aspects of memory access, Recall and Recognition. Recall is defined as the ability to retrieve an item from a list of items (words) originally presented during a previous learning phase, given an appropriate cue (cued RecalQ, or spontaneously (free RecalQ. Recognition is defined as the ability to successfully acknowledge that a certain item has or has not appeared in the tutorial list learned before. The main prospects of ANN modeling is that some parameter values, that in former, 'classical' models of memory retrieval (see e.g.

The goal has been to construct a supervised artificial neural network that learns incrementally an unknown mapping. As a result a network consisting ofa combination of ART2 and backpropagation is proposed and is called an "ART2/BP" network. The ART2 network is used to build and focus a supervised backpropagation network. The ART2/BP network has the advantage of being able to dynamically expand itself in response to input patterns containing new information. Simulation results show that the ART2/BP network outperforms a classical maximum likelihood method for the estimation of a discrete dynamic and nonlinear transfer function.

In a previous paper (Touretzky & Wheeler, 1990a) we showed how adding a clustering operation to a connectionist phonology model produced a parallel processing accountof certain "iterative" phenomena. In this paper we show how the addition of a second structuring primitive, syllabification, greatly increases the power of the model. We present examples from a non-Indo-European language that appear to require rule ordering to at least a depth of four. By adding syllabification circuitryto structure the model's perception of the input string, we are able to handle these examples with only two derivational steps. We conclude that in phonology, derivation can be largely replaced by structuring.

This paper is a summary of SPRINT project aims and results. The project focus on the use of neuro-computing techniques to tackle various problems that remain unsolved in speech recognition. First results concern the use of feedforward netsfor phonetic units classification, isolated word recognition, and speaker adaptation.

Empirically, generalization between a training and a test stimulus falls off in close approximation to an exponential decay function of distance between the two stimuli in the "stimulus space" obtained by multidimensional scaling. Mathematically, thisresult is derivable from the assumption that an individual takes the training stimulus to belong to a "consequential" region that includes that stimulus but is otherwise of unknown location, size, and shape in the stimulus space (Shepard, 1987). As the individual gains additional information about the consequential region-by finding other stimuli to be consequential or nOl-the theory predicts the shape of the generalization function to change toward the function relating actual probability of the consequence to location in the stimulus space. This paper describes a natural connectionist implementation of the theory, and illustrates how implications of the theory for generalization, discrimination, and classification learning can be explored by connectionist simulation. 1 THE THEORY OF GENERALIZATION Because we never confront exactly the same situation twice, anything we have learned in any previous situation can guide us in deciding which action to take in the present situation only to the extent that the similarity between the two situations is sufficient to justify generalization of our previous learning to the present situation. Accordingly, principles of generalization must be foundational for any theory of behavior. In Shepard (1987) nonarbitrary principles of generalization were sought that would be optimum in any world in which an object, however distinct from other objects, is generally a member of some class or natural kind sharing some dispositional property of potential consequence for the individual. A newly encountered plant or animal might be edible or 665 666 Shepard and Kannappan poisonous.

These experiments are designed to test whether average generalization performance can surpass the worst-case bounds obtained from formal learning theory using the Vapnik-Chervonenkis dimension (Blumer et al., 1989). We indeed find that, in some cases, the average generalization is significantly better than the VC bound: the approach to perfect performance is exponential in the number of examples m, rather than the 11m result of the bound. In other cases, we do find the 11m behavior of the VC bound, and in these cases, the numerical prefactor is closely related to prefactor contained in the bound.

Feedback connections are required so that the teacher signal on the output neurons can modify weights during supervised learning. Relaxation methods are needed for learning static patterns with full-time feedback connections. Feedback network learning techniques have not achieved wide popularity because of the still greater computational efficiency of back-propagation. We show by simulation that relaxation networks of the kind we are implementing in VLSI are capable of learning large problems just like back-propagation networks. A microchip incorporates deterministic mean-field theory learning as well as stochastic Boltzmann learning. A multiple-chip electronic system implementing these networks will make high-speed parallel learning in them feasible in the future.

This paper presents a neural network (NN) approach to the problem of stereopsis. The correspondence problem (finding the correct matches between the pixels of the epipolar lines of the stereo pair from amongst all the possible matches) is posed as a non-iterative many-to-one mapping. A two-layer feed forward NN architecture is developed to learn and code this nonlinear and complex mapping using the back-propagation learning rule and a training set. The important aspect of this technique is that none of the typical constraints such as uniqueness and continuity are explicitly imposed. All the applicable constraints are learned and internally coded by the NN enabling it to be more flexible and more accurate than the existing methods. The approach is successfully tested on several randomdot stereograms.It is shown that the net can generalize its learned mapping tocases outside its training set. Advantages over the Marr-Poggio Algorithm are discussed and it is shown that the NN performance is superIOr.

Santosh S. Venkatesh Electrical Engineering University of Pennsylvania Philadelphia, PA 19104 If patterns are drawn from an n-dimensional feature space according to a probability distribution that obeys a weak smoothness criterion, we show that the probability that a random input pattern is misclassified by a nearest-neighbor classifier using M random reference patterns asymptotically satisfies a PM(error) "" Poo(error) M2/n' for sufficiently large values of M. Here, Poo(error) denotes the probability of error in the infinite sample limit, and is at most twice the error of a Bayes classifier. Although the value of the coefficient a depends upon the underlying probability distributions, the exponent of M is largely distribution free.We thus obtain a concise relation between a classifier's ability to generalize from a finite reference sample and the dimensionality of the feature space, as well as an analytic validation of Bellman's well known "curse of dimensionality." 1 INTRODUCTION One of the primary tasks assigned to neural networks is pattern classification.