Machine Learning - Special issue on inductive transfer
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
The training of neural classifiers with condensed datasets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bias learning, knowledge sharing
IEEE Transactions on Neural Networks
Improving learning by using artificial hints
Neurocomputing
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In real life, the task learning is reinforced by the related tasks that we have learned or that we learn at the same time. This scheme applied to Artificial Neural Networks (ANN) is known with the name of Multitask Learning (MTL). So, the information coming from the related secondary tasks provide a bias to the main task, which improves its performances versus a Single-Task Learning (STL) scheme. However, this implies a bigger complexity. Data Editing procedures are used to reduce the algorithmic complexity, obtaining an outstanding samples set from the original set. This edited set gets the performance very fast. In this paper we combine MTL with Data Editing, so we can approach the small samples set training in an MTL scheme.