The Community for Technology Leaders
Green Image
Issue No. 02 - February (2010 vol. 32)
ISSN: 0162-8828
pp: 193-205
Gianluigi Pillonetto , University of Padova, Padova
Francesco Dinuzzo , University of Pavia, Pavia
Giuseppe De Nicolao , University of Pavia, Pavia
Standard single-task kernel methods have recently been extended to the case of multitask learning in the context of regularization theory. There are experimental results, especially in biomedicine, showing the benefit of the multitask approach compared to the single-task one. However, a possible drawback is computational complexity. For instance, when regularization networks are used, complexity scales as the cube of the overall number of training data, which may be large when several tasks are involved. The aim of this paper is to derive an efficient computational scheme for an important class of multitask kernels. More precisely, a quadratic loss is assumed and each task consists of the sum of a common term and a task-specific one. Within a Bayesian setting, a recursive online algorithm is obtained, which updates both estimates and confidence intervals as new data become available. The algorithm is tested on two simulated problems and a real data set relative to xenobiotics administration in human patients.
Collaborative filtering, multitask learning, mixed effects model, kernel methods, regularization, Gaussian processes, Kalman filtering, pharmacokinetic data.

F. Dinuzzo, G. De Nicolao and G. Pillonetto, "Bayesian Online Multitask Learning of Gaussian Processes," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 193-205, 2008.
84 ms
(Ver 3.3 (11022016))