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Hi, can someone recommend some opensource Python code for multi-task supervised learning? e.g. Multi-Task SVM or Multi-Task sparse multinomial regression or whatever comes to your mind. Thanks |
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Alexandre, my NUT project contains a (python) implementation of Ando & Zhangs ASO algorithm. The algorithm is pretty straight-forward: learn multiple linear predictors independently (via multiprocessing or Hadoop), then run SVD on the predictor matrix. best, Peter |
to give more details I have 2 objectives :
benchmark a solution for predicting multiple binary outputs jointly.
see what's available out there in order to add this feature to scikit-learn
do not hesitate to promote your own work !
I am also interested by answers like : "I would try with [?] (it's implemented there in Matlab)" or "I would implement [?] it is fairly straightforward"
Do you have some reference papers in mind?
sure.
Caruana. Multitask Learning: A Knowledge-Based Source of Inductive Bias. (1995)
Ben-David et Schuller. Exploiting task relatedness for multiple task learning. Learning Theory and Kernel Machines (2003) pp. 567-580
Ando et Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. The Journal of Machine Learning Research (2005) vol. 6 pp. 1817-1853
Evgeniou et Pontil. Regularized multi-task learning. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (2004) pp. 109-117
This interest follows a recent reading at NIPS 2010:
Decoding Ipsilateral Finger Movements from ECoG Signals in Humans Y. Liu, M. Sharma, C. Gaona, J. Breshears, j. Roland, z. Freudenburg, K. Weinberger, E. Leuthardt
do not hesitate to add more if I miss some key contributions to this problem.
Minor comment: there is a more complete article by Caruana in the journal 'Machine Learning'.