Abstract
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.
This work is partially supported by Ministerio de Educación y Ciencia under grant TEC2006-13338/TCM, and by Consejería de Educación y Cultura de Murcia under grant 03122/PI/05.
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Bueno-Crespo, A., Sánchez-García, A., Morales-Sánchez, J., Sancho-Gómez, JL. (2007). Multitask Learning with Data Editing. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_32
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DOI: https://doi.org/10.1007/978-3-540-73053-8_32
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