PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multitask Learning Using Regularized Multiple Kernel Learning
Mehmet Gönen, Melih Kandemir and Samuel Kaski
In: 2011 International Conference on Neural Information Processing (ICONIP 2011), 14-17 Nov 2011, Shanghai, China.


Empirical success of kernel-based learning algorithms is very much dependent on the kernel function used. Instead of using a single fixed kernel function, multiple kernel learning (MKL) algorithms learn a combination of different kernel functions in order to obtain a similarity measure that better matches the underlying problem. We study multitask learning (MTL) problems and formulate a novel MTL algorithm that trains coupled but nonidentical MKL models across the tasks. The proposed algorithm is especially useful for tasks that have different input and/or output space characteristics and is computationally very efficient. Empirical results on three data sets validate the generalization performance and the efficiency of our approach.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9188
Deposited By:Mehmet Gönen
Deposited On:21 February 2012