Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing

Jaques, N., Taylor, S., Sano, A., and Picard, R.W. "Multi-task, Multi-Kernel Learning for Estimating Individual Wellbeing" In Proc. NIPS Workshop on Multimodal Machine Learning, Montreal, Quebec, December 2015.


We apply a recently proposed technique – Multi-task Multi-Kernel Learning (MTMKL) – to the problem of modeling students’ wellbeing. Because wellbeing is a complex internal state consisting of several related dimensions, Multi-task learning can be used to classify them simultaneously. Multiple Kernel Learning is used to efficiently combine data from multiple modalities. MTMKL combines these approaches using an optimization function similar to a support vector machine (SVM). We show that MTMKL successfully classifies five dimensions of wellbeing, and provides performance benefits above both SVM and MKL.

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