Implement multitask and domain adaptation algorithms
by Sergey Lisitsyn for Shogun Machine Learning Toolbox
Multitask learning is a modern approach to machine learning that learns a problem together with other related problems at the same time, using a shared representation. This approach often leads to a better model for the main task, because it allows the learner to use the commonality among the tasks. The proposed project is about implementing various multitask learning algorithms for the Shogun toolbox.