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Beehive's goal is to make J2EE programming easier by building a simple object model on J2EE and Struts. Using the new JSR-175 annotations, Beehive reduces the coding necessary for J2EE. The initial Beehive project has three pieces. NetUI: An annotation-driven web application programming framework that is built atop Struts. NetUI centralizes navigation logic, state, metadata, and exception handling in a single encapsulated and reusable Page Flow Controller class. In addition, NetUI provides a set of JSP tags for rendering HTML / XHTML and higher-level UI constructs such as data grids and trees and has first-class integration with JavaServer Faces and Struts. Controls: A lightweight, metadata-driven component framework for building that reduces the complexity of being a client of enterprise resources. Controls provide a unified client abstraction that can be implemented to access a diverse set of enterprise resources using a single configuration model. In addition, Beehive includes a set of system controls that are abstractions for low-level J2EE resource APIs such as EJB, JMS, JDBC, and web services.

Performs one-class classification on a dataset. Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class. Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any classes. For more information, see: Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.