Machine Learning - Special issue on inductive transfer
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Convex multi-task feature learning
Machine Learning
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Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.