H3C 3A7 CANADA 2Department of Cognitive and Neural Systems, Boston University Boston, MA 02215 USA Abstract The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). The performance ofARTMAP-FD is tested on radar pulse data obtained in the field, and compared to that of the nearest-neighbor-based NEN algorithm and to a k 1 extension of NEN. 1 Introduction The recognition process involves both identification and familiarity discrimination. Consider, for example, a neural network designed to identify aircraft based on their radar reflections and trained on sample reflections from ten types of aircraft A . . . After training, the network should correctly classify radar reflections belonging to the familiar classes A . Familiarity discrimination is also referred to as "novelty detection," a "reject option," and "recognition in partially exposed environments."

The figure shows an ideal continuous loop from theory to feasibility understanding the problem of intelligence. In reality, the learning is also becoming a key to the study of interactions--as one might expect--are less artificial and biological vision. For example in years, both computer vision--which attempts 1990, ideas from the mathematics of learning to build machines that see--and visual neuroscience--which theory--radial basis function networks--suggested aims to understand how our a model for biological object recognition visual system works--are undergoing a fundamental that led to the physiological experiments change in their approaches. Visual neuroscience in cortex described later in the article. It was is beginning to focus on the mechanisms only later that the same idea found its way into that allow the cortex to adapt its the computer graphics applications described circuitry and learn a new task. In this article, we concentrate on one aspect of Vision systems that learn and adapt represent learning: supervised learning.

Here we sudy the continuous time, continuous state-space stochastic case, which covers a wide variety of control problems including target, viability, optimization problems (see [FS93], [KP95])}or which a formalism is the following.

The generalization ability of a neural network can sometimes be improved dramatically by regularization. To analyze the improvement one needs more refined results than the asymptotic distribution of the weight vector. Here we study the simple case of one-dimensional linear regression under quadratic regularization, i.e., ridge regression. We study the random design, misspecified case, where we derive expansions for the optimal regularization parameter and the ensuing improvement. It is possible to construct examples where it is best to use no regularization.

The generalization ability of a neural network can sometimes be improved dramatically by regularization. To analyze the improvement one needs more refined results than the asymptotic distribution of the weight vector. Here we study the simple case of one-dimensional linear regression under quadratic regularization, i.e., ridge regression. We study the random design, misspecified case, where we derive expansions for the optimal regularization parameter and the ensuing improvement. It is possible to construct examples where it is best to use no regularization.

The generalization ability of a neural network can sometimes be improved dramatically by regularization. To analyze the improvement one needs more refined results than the asymptotic distribution of the weight vector. Here we study the simple case of one-dimensional linear regression under quadratic regularization, i.e., ridge regression. We study the random design, misspecified case, where we derive expansions for the optimal regularization parameter and the ensuing improvement. It is possible to construct examples where it is best to use no regularization.

Here we sudy the continuous time, continuous state-spacestochastic case, which covers a wide variety of control problems including target, viability, optimization problems (see [FS93], [KP95])}or which a formalism is the following.

The generalization ability of a neural network can sometimes be improved dramatically by regularization. To analyze the improvement oneneeds more refined results than the asymptotic distribution ofthe weight vector. Here we study the simple case of one-dimensional linear regression under quadratic regularization, i.e., ridge regression. We study the random design, misspecified case, where we derive expansions for the optimal regularization parameter andthe ensuing improvement. It is possible to construct examples where it is best to use no regularization.

The generalization ability of a neural network can sometimes be improved dramatically by regularization. To analyze the improvement oneneeds more refined results than the asymptotic distribution ofthe weight vector. Here we study the simple case of one-dimensional linear regression under quadratic regularization, i.e., ridge regression. We study the random design, misspecified case, where we derive expansions for the optimal regularization parameter andthe ensuing improvement. It is possible to construct examples where it is best to use no regularization.

Now completing its first year, the High-Performance Knowledge Bases Project promotes technology for developing very large, flexible, and reusable knowledge bases. The project is supported by the Defense Advanced Research Projects Agency and includes more than 15 contractors in universities, research laboratories, and companies. The evaluation of the constituent technologies centers on two challenge problems, in crisis management and battlespace reasoning, each demanding powerful problem solving with very large knowledge bases. This article discusses the challenge problems, the constituent technologies, and their integration and evaluation.