When large numbers of alerts are reported by intrusion detection (ID) systems in very fine granularity, it prevents system administrators from handling the alerts effectively. This in turn degrades the usability of an intrusion detection system. Aside from detection, timely responses of...

We present a novel maximum-likelihood-based algorithm for estimating the distribution of alignment scores from the scores of unrelated sequences in a database search. Using a new method for measuring the accuracy of p-values, we show that our maximum-likelihood-based algorithm is more accurate...

Several models in econometrics and finance have been proven to be computationally intractable due to their complexity. In this dissertation, we propose an evolutionary-genetic-algorithm for solving these types of problems. We extend the models so that less restrictive assumptions are required...

A methodology is presented with examples of the productivity, the staffing required, the resultant productivity, and costs that can be obtained for hospital units that are subject to random work demands such as laboratory, radiology, physical therapy, and nuclear medicine. The methodology...

The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations of classification-learning performance and then uses those observations to create statistical models. The models can be used to predict the...

A framework for developing marketing category management decision support systems (DSS) based upon the Bayesian Vector Autoregressive (BVAR) model is extended. Since the BVAR model is vulnerable to permanent and temporary shifts in purchasing patterns over time, a form that can correct for the...

Probabilistic graphical models, by making conditional independence assumptions, can represent complex joint distributions in a factorized form. However, in large problems graphical models often run into two issues. First, in non-treelike graphs, computational issues frustrate exact inference....

We first review the concepts fundamental to the statistical inference procedures using nonparametric regression models. The global error properties of an estimator over its parameter space are employed to define a general framework that puts various existing optimality criteria and heuristics...

Gaussian mixture models have been used extensively in model-based clustering. It is well known that the likelihood function for this model is unbounded and the global MLE does not exist. Existing methods confine the parameter estimates in the interior of the parameter space and the MLE is shown...

For hyperspectral data classification, the avoidance of singularity of covariance estimates or excessive near singularity estimation error due to limited training data is a key problem. This study is intended to solve problem via regularized covariance estimators and feature extraction...