This paper applies the theory of Probably Approximately Correct (PAC) learning to multiple output feedforward threshold networks in which the weights conform to certain equivalences. It is shown that the sample size for reliable learning can be bounded above by a formula similar to that required for single output networks with no equivalences. The best previously obtainedbounds are improved for all cases.

Biological retinas extract spatial and temporal features in an attempt to reduce the complexity of performing visual tasks. We have built and tested a silicon retina which encodes several useful temporal features found in vertebrate retinas.The cells in our silicon retina are selective to direction, highly sensitive to positive contrast changes around an ambient light level, and tuned to a particular velocity. Inhibitory connections in the null direction performthe direction selectivity we desire. This silicon retina is on a 4.6 x 6.8mm die and consists of a 47 x 41 array of photoreceptors.

Networks for reconstructing a sparse or noisy function often use an edge field to segment the function into homogeneous regions, This approach assumes that these regions do not overlap or have disjoint parts, which is often false. For example, images which contain regions split by an occluding objectcan't be properly reconstructed using this type of network. We have developed a network that overcomes these limitations, using support maps to represent the segmentation of a signal. In our approach, the support ofeach region in the signal is explicitly represented. Results from an initial implementation demonstrate that this method can reconstruct images and motion sequences which contain complicated occlusion.

In this work, the problem of establishing routes through random MINs (RMINs) in a shared-memory, distributed computing system is addressed.

This paper considers the problem of expressing predicate calculus in connectionist networksthat are based on energy minimization. Given a firstorder-logic knowledgebase and a bound k, a symmetric network is constructed (like a Boltzman machine or a Hopfield network) that searches for a proof for a given query. If a resolution-based proof of length no longer than k exists, then the global minima of the energy function that is associated with the network represent such proofs. The network that is generated is of size cubic in the bound k and linear in the knowledge size. There are no restrictions on the type of logic formulas that can be represented.

Feedforward networks composed of units which compute a sigmoidal function ofa weighted sum of their inputs have been much investigated. We tested the approximation and estimation capabilities of networks using functions more complex than sigmoids. Three classes of functions were tested: polynomials, rational functions, and flexible Fourier series. Unlike sigmoids,these classes can fit nonmonotonic functions. They were compared on three problems: prediction of Boston housing prices, the sunspot count, and robot arm inverse dynamics. The complex units attained clearlysuperior performance on the robot arm problem, which is a highly nonmonotonic, pure approximation problem. On the noisy and only mildly nonlinear Boston housing and sunspot problems, differences among the complex units were revealed; polynomials did poorly, whereas rationals and flexible Fourier series were comparable to sigmoids. 1 Introduction

This is an extension of previous work on recognizing isolated digits.

Mark Fanty, Ronald A. Cole and Krist Roginski Center for Spoken Language Understanding Oregon Graduate Institute of Science and Technology 19600 N.W. Von Neumann Dr., Beaverton, OR 97006 Abstract A recognition system is reported which recognizes names spelled over the telephone with brief pauses between letters. The system uses separate neural networks to locate segment boundaries and classify letters. The letter scores are then used to search a database of names to find the best scoring name. The speaker-independent classification rate for spoken letters is89%. The system retrieves the correct name, spelled with pauses between letters, 91 % of the time from a database of 50,000 names. 1 INTRODUCTION The English alphabet is difficult to recognize automatically because many letters sound alike; e.g., BID, PIT, VIZ and F IS.

A CCO based signal processing IC that computes a fully parallel single quadrant vector-matrix multiplication has been designed and fabricated with a 2j..un CCO/CMOS process. The device incorporates an array of Charge Coupled Devices (CCO) which hold an analog matrix of charge encoding the matrix elements. Input vectors are digital with 1 - 8 bit accuracy.

Another class of nonlinear algorithms, exemplified by CART (Breiman, Friedman, Olshen, & Stone, 1984) and MARS (Friedman, 1990), generalizes classicaltechniques by partitioning the training data into non-overlapping regions and fitting separate models in each of the regions. These two classes of algorithms extendlinear techniques in essentially independent directions, thus it seems worthwhile to investigate algorithms that incorporate aspects of both approaches to model estimation. Such algorithms would be related to CART and MARS as multilayer neural networks are related to linear statistical techniques.