John C. Stutz

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We describe AutoClass, an approach to unsupervised classiication based upon the classical mixture model, supplemented by a Bayesian method for determining the optimal classes. We include a moderately detailed exposition of the mathematics behind the AutoClass system. We emphasize that no current unsupervised classiication system can produce maximally useful(More)
This paper describes AutoClass H, a program for automatically discovering (inducing) classes from a database, based on a Bayesian statistical technique which automatically determines the most probable number of classes, their probabilistic descriptions, and the probability that each object is a member of each class. AutoClass has been tested on several(More)
Local search algorithms for combinatorial search problems frequently encounter a sequence of states in which it is impossible to improve the value of the objective function; moves through these regions, called plateau moves, dominate the time spent in local search. We analyze and characterize plateaus for three di erent classes of randomly generated Boolean(More)
This paper describes a Bayesian method for constructing a super-resolved surface model by combining information from a set of images of the given surface. We develop the theory and algorithms in detail for the 2-D reconstruction problem, appropriate for the case where all images are taken from roughly the same direction and under similar lighting(More)
This paper describes a Bayesian technique for unsupervised classification of data and its computer implementation, Autoclass. Given real valued or discrete data, AutoClass automatically determines the most probable number of classes present in the data, the most probable descriptions of those classes, and each object's probability of membership in each(More)
The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the(More)
Most complex aerospace systems have many text reports on safety, maintenance, and associated issues. The Aviation Safety Reporting System (ASRS) spans several decades and contains over 700 000 reports. The Aviation Safety Action Plan (ASAP) contains over 12 000 reports from various airlines. Problem categorizations have been developed for both ASRS and ASAP(More)
Generative models of natural images have long been used in computer vision. However, since they only describe the statistics of 2D scenes, they fail to capture all the properties of the underlying 3D world. Even though such models are sufficient for many vision tasks, a 3D scene model is needed when it comes to inferring a 3D object or its characteristics.(More)
The task of inferr ing a set of classes and class descriptions most l ikely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. W i t h i n this framework, and using various mathematical and algorithmic approximations, the Au toClass system searches for the most probable classifications, automatically(More)
i Forward Over the past five years portions of the US electric utility industry have experienced a sweeping series of changes referred to as restructuring. These changes affect both the structure of the organizations involved in the electric utility industry, and the institutional arrangements by which decisions involving planning and pricing are made. As(More)