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YET ANOTHER IMPLEMENTATION OF LEVENSHTEIN DISTANCE GenericLevenshtein is an implementation of Minimum Edit Distance, also called Levenshtein Distance, written by Ramon Ziai and Niels Ott. This algorithm is very popular and it is often used to compute the similarity of strings. The difference in the presented implementation is that it can operate on sequences of any Java object implementing equals(Object). So no matter if you want to compare genome sequences or sequences of numbers, or just strings, here you go! Furthermore, the costs of the replace, insert, and delete operations can be customized by implementing the simple WeightCalculator<T> interface. In that case it is not a requirement to rely on equals(Object) as your implementation can do whatever you like it to do in oder to compare objects.

This library aims to provide a central access point to the services exposed on the ESB without forcing each part of the system to know the actual location of the ESB and the names of the exposed endpoints. For this sake it allows to map service URI (ie referring to service in webLab taxonomy) to the actual exposed URL on the ESB within the context of a specific project. The library and the configuration should be added in portal lib in order to be central and available to any portlets. The mapping is done in a simple spring config file "webLabClient.xml" which must be accessible in JAVA classpath.

JDOM is a Java-oriented object model which models XML documents. It provides a Java-centric means of generating and manipulating XML documents. While JDOM interoperates well with existing standards such as the Simple API for XML (SAX) and the Document Object Model (DOM), it is not an abstraction layer or enhancement to those APIs. Rather, it seeks to provide a robust, light-weight means of reading and writing XML data without the complex and memory-consumptive options that current API offerings provide. built July 23 2009

Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified. For more information, see: Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.