Training a multilayer neural net by back-propagation is slow and requires arbitrary choices regarding the number of hidden units and layers. This paper describes an algorithm which is much faster than back-propagation and for which it is not necessary to specify the number of hidden units in advance. The relationship with other fast pattern recognition algorithms, such as algorithms based on k-d trees, is mentioned. The algorithm has been implemented and tested on articial problems such as the parity problem and on real problems arising in speech recognition. Experimental results, including training times and recognition accuracy, are given. Generally, the algorithm achieves accuracy as good as or better than nets trained using back-propagation, and the training process is much faster than back-propagation. Accuracy is comparable to that for the \nearest neighbour" algorithm, which is slower and requires more storage space. Comments Only the Abstract is given here. The full paper ap...

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Pattern recognition methods and hidden Markov models can be effective tools for online health monitoring of communications systems. Previous work has assumed that the states in the system model are exhaustive. This can be a significant drawback in real-world fault monitoring applications where it is if not impossible to model all the possible fault states of the system in advance. In this paper a method is described for extending the Markov monitoring approach to allow for unknown or novel states which can not be accounted for when the model is being designed, The method is described and evaluated on data from one of the Jet Propulsion Laboratory 's Deep Space Network antennas. The experimental results indicate that the method is both practical and effective, allowing both discrimination between known states and detection of previously unknown fault conditions.

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Scientific visualization is the process of using graphical images to form succinct and lucid representations of numerical data. Visualization has proven to be a useful method for understanding both learning and computation in artificial neural networks. While providing a powerful and general technique for inductive learning, artificial neural networks are difficult to comprehend because they form representations that are encoded by a large number of real-valued parameters. By viewing these parameters pictorially, a better understanding can be gained of how a network maps inputs into outputs. In this article, we survey a number of visualization techniques for understanding the learning and decision-making processes of neural networks. We also describe our work in knowledgebased neural networks and the visualization techniques we have used to understand these networks. In a knowledge-based neural network, the topology and initial weight values of the network are determined by an approxim...

A variety of approaches to language identification, based on (a) acoustic features, (b) broad-category segmentation, and (c) fine phonetic classification, are introduced. These approaches are evaluated in terms of their ability to distinguish between English and Japanese utterances spoken over a telephone channel. It is found that the best performance (86.3 % accurate classification of utterances with a mean length of 13.4 sec) is obtained when fine phonetic features are employed. In addition, the results show the importance of discriminatory training rather than likelihood estimation. 1. INTRODUCTION As developments in telecommunications and long-distance travel cause national borders to become increasingly transparent, the ability to identify which language is being spoken is growing in importance. The utility of tasks such as directory assistance or automatic translation is, for instance, improved substantially by the availability of a means of identifying which language is being s...

Parallel environments consisting of a network of heterogeneous workstations introduce an inherently dynamic environment that differs from multicomputers. Workstations are usually considered “shared ” resources while multicomputers provide dedicated processing power. The number of workstations available for use is continually changing; the parallel machine presented by the network is in effect continually reconfiguring itself. Application programs must effectively adapt to the changing number of processing nodes while maintaining computational efficiency. This paper examines methods for adapting to this dynamic environment within the framework of explicit message passing under the data parallel programming model. We present four requirements which we feel a method must satisfy. Several potential methods are examined within the framework and evaluated according to how well they address the defined requirements. An application-level technique called Application Data Movement (ADM) is described. Although this technique puts much of the responsibility of adaptation on the application programmer, it has the advantage of running on heterogeneous workstations. Related work, such as Dataparallel C and Piranha, is also examined and compared to ADM. The application of the ADM methodology to a real application, a neural-network classifier based on conjugate-gradient optimization, is outlined and discussed. Preliminary results are presented and analyzed. The computation has been shown to achieve in excess of 70 MFLOPS under quiet conditions on a network of nine heterogeneous machines, two HP 9000/720s, two DEC Alphas, and five Sun SPARCstation 10s, while maintaining an efficiency of nearly 80%. 1 1

A neurally based adaptive controller for a 6 degrees of freedom (DOF) robot manipulator with only rotary joints and a hand-held camera is described. The task of the system is to place the manipulator directly above an object that is observed by the camera (i.e., 2D hand-eye coordination). The requirement of adaptivity results in a system which does not make use of any inverse kinematics formulas or other detailed knowledge of the plant; instead, it should be self-supervising and adapt on-line. The proposed neural system will directly translate the preprocessed sensory data to joint displacements. It controls the plant in a feedback loop. The robot arm may make a sequence of moves before the target is reached, when in the meantime the network learns from experience. The network is shown to adapt quickly (in only tens of trials) and form a correct mapping from input to output domain. 1 Introduction Traditionally, when a robot manipulator controller receives sensory information based on ...

This paper reports the results of the development, deployment and testing of a large spoken-language dialogue application for use by the general public. We built an automated spoken questionnaire for the U.S. Bureau of the Census. In the project's first phase, the basic recognizers and dialogue system were developed using 4,000 calls. In the second phase, the system was adapted to meet Census Bureau requirements and deployed in the Bureau's 1995 national test of new technologies. In the third phase, we refined the system and showed empirically that an automated spoken questionnaire could successfully collect and recognize census data, and that subjects preferred the spoken system to written questionnaires. Our large data collection effort and two subsequent field tests showed that, when questions are asked correctly, the answers contain information within the desired response categories about 99 percent of the time. 1 Introduction Every ten years, the U.S. Bureau of the Census (hereaf...

xii 1 Introduction 1 1.1 Background : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.1.1 Nature of the Problem : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.1.2 The Difficulties: Challenges to LID : : : : : : : : : : : : : : : : : : : 5 1.2 Related Work : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 1.2.1 Early Work: 1973--1992 : : : : : : : : : : : : : : : : : : : : : : : : : 7 1.2.2 Current Activities: 1992--present : : : : : : : : : : : : : : : : : : : : 9 1.2.3 The Problems : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 11 1.3 An Approach to Language Identification based on language-dependent phone recognition. : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 12 1.3.1 Finding a Good Modeling Unit : : : : : : : : : : : : : : : : : : : : : 12 1.3.2 The Baseline System : : : : : : : : : : : : : : : : : : : : : : : : : : : 13 1.3.3 Contributions: Methods Proposed to Improve the Baseline...