genCore is a part of GAF (Genetic Algorithms Framework), which is a cross-platform framework for using Genetic Algorithms for solutions. It is written in Java and uses convenient plug-in features for every phase in the genetic development, while maintaining an easy-to-use API for easy integration into applications. The genCore module is the heart of GAF, as it is the engine for the GA itself.
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). This approach is one of the most efficient and simple to combine continuous and nominal values. This implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable amount of time/memory.
imaverage uses the viewing frequency and viewing time from a spawned image viewer to build a dynamic database entry for images to gauge their relative preference for a given user. Once the entries have been created, imaverage will continue to show images randomly, with dynamic preference weights. On average, your favorite images should show up most frequently.
Isobel is a framework to build complex information retrieval and analysis systems. Isobel can be functionally divided in two subsytems, Isobel Gatherer (the crawling and filtering subsystem) and Isobel Analyzer (the analysis subsystem). The two subsytems can also be used separately. Isobel Gatherer offers ready-to-use services like content fetching, scheduling, document format conversion, Hyperlink graph storage and analysis, content storage and indexing. A programmer may easily add new services. Isobel Analyzer uses the IBM UIMA architecture to reuse the analysis components developed for this architecture.