TY - GEN
T1 - Semi-supervised penalized output kernel regression for link prediction
AU - Brouard, Céline
AU - D'Alché-Buc, Florence
AU - Szafranski, Marie
PY - 2011/1/1
Y1 - 2011/1/1
N2 - 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 using numerical experiments on artificial networks and two real applications using a very low percentage of labeled data in a transductive setting.
AB - 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 using numerical experiments on artificial networks and two real applications using a very low percentage of labeled data in a transductive setting.
UR - https://www.scopus.com/pages/publications/80053456365
M3 - Conference contribution
AN - SCOPUS:80053456365
SN - 9781450306195
T3 - Proceedings of the 28th International Conference on Machine Learning, ICML 2011
SP - 593
EP - 600
BT - Proceedings of the 28th International Conference on Machine Learning, ICML 2011
PB - Association for Computing Machinery
T2 - 28th International Conference on Machine Learning, ICML 2011
Y2 - 28 June 2011 through 2 July 2011
ER -