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The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.<\/jats:p>","DOI":"10.1155\/2014\/317056","type":"journal-article","created":{"date-parts":[[2014,4,18]],"date-time":"2014-04-18T01:14:58Z","timestamp":1397783698000},"page":"1-13","source":"Crossref","is-referenced-by-count":9,"title":["Mixed-Norm Regularization for Brain Decoding"],"prefix":"10.1155","volume":"2014","author":[{"given":"R.","family":"Flamary","sequence":"first","affiliation":[{"name":"Laboratoire Lagrange, UMR7293, Universit\u00e9 de Nice, 00006 Nice, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Jrad","sequence":"additional","affiliation":[{"name":"Gipsa Lab, Domaine Universitaire BP 46, 38402 Saint Martin d'H\u00e8res, 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