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Conference papers
Semi-supervised Penalized Output Kernel Regression for Link Prediction
Abstract : Link prediction is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory with vector- valued functions, we establish a new repre- senter theorem devoted to semi-supervised least square regression. We then apply it to get a new model (POKR: Penalized Output Kernel Regression) and show its relevance us- ing numerical experiments on arti cial net- works and two real applications using a very low percentage of labeled data in a transduc- tive setting.
Cited literature [24 references]
https://hal.archives-ouvertes.fr/hal-00654123
Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Thursday, November 28, 2013 - 5:20:33 PM
Last modification on : Monday, April 4, 2022 - 10:00:01 AM
Long-term archiving on: : Monday, March 3, 2014 - 1:50:31 PM
Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Thursday, November 28, 2013 - 5:20:33 PM
Last modification on : Monday, April 4, 2022 - 10:00:01 AM
Long-term archiving on: : Monday, March 3, 2014 - 1:50:31 PM
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