Cetin Savkli

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The problem of categorical data analysis in high dimensions is considered. A discussion of the fundamental difficulties of probability modeling is provided, and a solution to the derivation of high dimensional probability distributions based on Bayesian learning of clique tree decomposition is presented. The main contributions of this paper are an automated(More)
The long-term goal of this project is to provide a flexible, accurate and extensible automated target recognition (ATR) system for use with a variety of imaging and non-imaging sensors. Such an ATR system, once it achieves a high level of performance, can relieve human operators from the tedious business of pouring over vast quantities of mostly mundane(More)
Autonomic computing for spacecraft ground systems increases the system reliability and reduces the cost of spacecraft operations and software maintenance. In this paper, we present an autonomic computing solution for spacecraft ground systems at NASA Goddard Space Flight Center (GSFC), which consists of an open standard for the message oriented architecture(More)
We present a new method of generating mixture models for data with categorical attributes. The keys to this approach are an entropy-based density metric in categorical space and annealing of high-entropy/low-density components from an initial state with many components. Pruning of low-density components using the entropy-based density allows GALILEO to(More)
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