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Rachel Lomasky and Carla Brodley address problems in the two areas of Machine Learning and Classification. A new class of supervised learning processes called Active Class Selection(ACS) addresses the question: if one can collect n additional training instances, how should they be distributed with respect to class? Working with Chemistry's Walt Laboratory at Tufts University they train an artificial nose to discriminate vapors. They use Active Class Selection to choose which training data to generate. And In the area of Active Learning they are interested in the development of tools to determine which Active Learning methods will work best for the problem at hand. They introduced an entropy-based measure, Average Pool Uncertainty, for assessing the online progress of active learning. The motivating problem of this research is the labeling of the Earth's surface to create a land cover classifier. They would like to determine when labeling more of the map will not contribute to an increase in accuracy. Both Active Class Selection and Active Learning are CPU-intensive. They require working with large datasets. Additionally, experiments are conducted with several methods, each with a large range of parameters. Without the cluster, their research would be so time-consuming as to be impractical.