Neural Computation
Neural networks for pattern recognition
Neural networks for pattern recognition
Machine Learning - Special issue on inductive transfer
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Multitask learning
Multitask Learning with Data Editing
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Inductive transfer with context-sensitive neural networks
Machine Learning
The training of neural classifiers with condensed datasets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
| Hi-index | 0.01 |
In Multi-Task Learning (MTL), when several related tasks are learned at the same time considering one of them as the main task and the others as secondary ones, there is a transfer of positive information that improves the performance of the main one. However, not only does the difficulty of finding the relationship among different tasks pose a problem in real applications, but also knowing the kind of relationship among them. This paper presents a new method to generate artificial hints (subsets from the original data set) that helps the learning of the main task when all of them are learned simultaneously (as in a MTL scheme). Thus, although these hints cannot be strictly considered as secondary tasks, they will act as guides for the main one. The results obtained with toy and real problems show the advantages of the proposed method. In particular, a faster convergence, a very good performance, and a reduction in the likelihood of being trapped in a local minimum are achieved.