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...

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...

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...