Automated monitoring of vigilance in attention intensive tasks such as air traffic control or sonar operation is highly desirable. As the operator monitorsthe instrument, the instrument would monitor the operator, insuring against lapses. We have taken a first step toward this goal by using feedforwardneural networks trained with backpropagation to interpret event related potentials (ERPs) and electroencephalogram (EEG) associated withperiods of high and low vigilance. The accuracy of our system on an ERP data set averaged over 28 minutes was 96%, better than the 83% accuracy obtained using linear discriminant analysis. Practical vigilance monitoring will require prediction over shorter time periods. We were able to average the ERP over as little as 2 minutes and still get 90% correct prediction of a vigilance measure. Additionally, we achieved similarly good performance using segments of EEG power spectrum as short as 56 sec.

A network model with temporal sequencing and state-dependent modulatory featuresis described. The model is motivated by neurocognitive data characterizing different states of waking and sleeping. Computer studies demonstrate how unique states of sequencing can exist within the same network under different aminergic and cholinergic modulatory influences. Relationships between state-dependent modulation, memory, sequencing and learning are discussed.

The former characterizes their "Present Address: Department of Electrical Computer and System Engineering, Rensselaer Polytech Institute, Troy, NY 12180.

We present the "Multi-State Time Delay Neural Network" (MS-TDNN) as an extension of the TDNN to robust word recognition.

Learning is posed as a problem of function estimation, for which two principles ofsolution are considered: empirical risk minimization and structural risk minimization. These two principles are applied to two different statements ofthe function estimation problem: global and local. Systematic improvements in prediction power are illustrated in application to zip-code recognition.

We derive criteria for training adaptive classifier networks to perform unsupervised dataanalysis. The first criterion turns a simple Gaussian classifier into a simple Gaussian mixture analyser. The second criterion, which is much more generally applicable, is based on mutual information.

Automatic language identification is the rapid automatic determination of the language beingspoken, by any speaker, saying anything. Despite several important applications of automatic language identification, this area has suffered from a lack of basic research and the absence of a standardized, public-domain database of languages. It is well known that languages have characteristic sound patterns. Languages have been described subjectively as "singsong", "rhythmic", "guttural", "nasal" etc. The key to solving the problem of automatic language identification is the detection and exploitation of such differences between languages. We assume that each language in the world has a unique acoustic structure, and that this structure can be defined in terms of phonetic and prosodic features of speech.

Ying Zhao Christopher G. Atkeson The Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract This paper will address an important question in machine learning: What kind of network architectures work better on what kind of problems? A projection pursuit learning network has a very similar structure to a one hidden layer sigmoidal neural network. A general method based on a continuous version of projection pursuit regression is developed to show that projection pursuit regression works better on angular smooth functions thanon Laplacian smooth functions. There exists a ridge function approximation scheme to avoid the curse of dimensionality for approximating functionsin L 2(¢d). 1 INTRODUCTION Projection pursuit is a nonparametric statistical technique to find "interesting" low dimensional projections of high dimensional data sets. It has been used for nonparametric fitting and other data-analytic purposes (Friedman and Stuetzle, 1981, Huber, 1985).

Automatic determination of proper neural network topology by trimming oversized networks is an important area of study, which has previously been addressed using a variety of techniques. In this paper, we present Information Measure Based Skeletonisation (IMBS), a new approach to this problem where superfluous hidden units are removed based on their information measure (1M). This measure, borrowed from decision tree induction techniques,reflects the degree to which the hyperplane formed by a hidden unit discriminates between training data classes. We show the results of applying IMBS to three classification tasks and demonstrate that it removes a substantial number of hidden units without significantly affecting network performance.

Thesedevices are implanted and perform three types of actions: l.monitor the heart 2.to pace the heart 3.to apply high energy/high voltage electric shock 1bey sense the electrical activity of the heart through leads attached to the heart tissue. Two types of sensing are commooly used: Single Chamber: Lead attached to the Right Ventricular Apex (RVA) Dual Chamber: An additional lead is attached to the High Right Atrium (HRA). The actions performed by defibrillators are based on the outcome of a classification procedure based on the heart rhythms of different heart diseases (abnormal rhythms or "arrhythmias").