Connection between SVM+ and multi-task learning
Lichen Liang, Vladimir Cherkassky · 2008
Exploiting additional information to improve traditional inductive learning is an active research in machine learning. When data are naturally separated into groups, SVM+[7] can effectively utilize this structure information to improve generalization. Alternatively, we can view learning based on data from each group as an individual task, but all these tasks are somehow related; so the same problem can also be formulated as a multi-task learning problem. Following the SVM+ approach, we propose a new multi-task learning algorithm called svm+MTL, which can be thought as an adaptation of SVM+ for solving MTL problem. The connections between SVM+ and svm+MTL are discussed and their performance is compared using synthetic data sets.