This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of...

Bayesian nonparametric methods are useful for modeling data without having to define the complexity of the entire model a priori, but rather allowing for this complexity to be determined by the data. Two problems considered in this dissertation are the number of components in a mixture model,...

Along with the growth of electronic commerce has come an interest in developing autonomous trading agents. Often, such agents must interact directly withother market participants, and so the behavior of these participants must be taken into account when designing agent strategies. One common...

The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the...

Extracting information from text is the task of obtaining structured, machineprocessable facts from information that is mentioned in an unstructured manner.It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or...

Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or...