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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Caruana, R., Baluja, S., Mitchell, T.: Using the future to ’sortout’ the present: Rankprop and multitask learning for medical risk evaluation. In: Advanced in Neural Information Processing Systems, vol. 8, pp. 959–965 (1996)
Caruana, R.: Algorithms and applications for multitask learning. In: International Conference on Machine Learning, pp. 87–95 (1996)
Caruana, R.: Multitask learning. Machine Learning 28, 41–75 (1997)
Caruana, R.: Learning many related tasks at the same time with backpropagation. In: Advanced in Neural Information Processing Systems, pp. 656–664 (1995)
Ghosn, J., Bengio, Y.: Bias learning, knowledge sharing. IEEE Transactions on Neural Networks 14, 87–95 (2003)
Silver, D.L., Mercer, R.E.: Selective functional transfer: Inductive bias from related tasks. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, Cancun, Mexico, pp. 182–191. ACTA Press (2001)
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, New York (1996)
Rockett, P., Choi, S.: The training of neural classifiers with condensed datasets. IEEE Transactions on Systems, Man, and Cybernetics - part B: Cybernetics 32, 202–206 (2002)
Hart, P.E.: The condensed nearest neighbour rule. IEEE Transactions on Information Theory 14, 515–516 (1968)
Hand, B.G., Batchelor, D.J.: An edited condensed nearest neighbour rule. Information Sciences 14, 171–180 (1978)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-73053-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73052-1
Online ISBN: 978-3-540-73053-8
eBook Packages: Computer ScienceComputer Science (R0)