Abstract
In Multitask Learning (MTL), a task is learned together with other related tasks, producing a transfer of information between them which can be advantageous for learning of the first one. However, rarely can solve a problem under an MTL scheme since no data are available that satisfying the conditions that need a MTL scheme. This paper presents a method to detect related tasks with the main one that allow to implement a multitask learning scheme. The method use the advantages of the Extreme Learning Machine and selects the secondary tasks without testing/error methodologies that increase the computational complexity.
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Bueno-Crespo, A., Menchón-Lara, RM., Sancho-Gómez, JL. (2015). Related Tasks Selection to Multitask Learning Schemes. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_23
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DOI: https://doi.org/10.1007/978-3-319-18833-1_23
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