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Kernel Constrained Covariance for Dependence Measurement
Arthur Gretton, Alexander Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos LogothetisProceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, PMLR R5:112-119, 2005.
Cite this Paper
BibTeX
@InProceedings{pmlr-vR5-gretton05a,
title = {Kernel Constrained Covariance for Dependence Measurement},
author = {Gretton, Arthur and Smola, Alexander and Bousquet, Olivier and Herbrich, Ralf and Belitski, Andrei and Augath, Mark and Murayama, Yusuke and Pauls, Jon and Sch\"olkopf, Bernhard and Logothetis, Nikos},
booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics},
pages = {112--119},
year = {2005},
editor = {Cowell, Robert G. and Ghahramani, Zoubin},
volume = {R5},
series = {Proceedings of Machine Learning Research},
month = {06--08 Jan},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/r5/gretton05a/gretton05a.pdf},
url = {https://proceedings.mlr.press/r5/gretton05a.html},
note = {Reissued by PMLR on 30 March 2021.}
}
Endnote
%0 Conference Paper
%T Kernel Constrained Covariance for Dependence Measurement
%A Arthur Gretton
%A Alexander Smola
%A Olivier Bousquet
%A Ralf Herbrich
%A Andrei Belitski
%A Mark Augath
%A Yusuke Murayama
%A Jon Pauls
%A Bernhard Schölkopf
%A Nikos Logothetis
%B Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics
%C Proceedings of Machine Learning Research
%D 2005
%E Robert G. Cowell
%E Zoubin Ghahramani
%F pmlr-vR5-gretton05a
%I PMLR
%P 112--119
%U https://proceedings.mlr.press/r5/gretton05a.html
%V R5
%Z Reissued by PMLR on 30 March 2021.
APA
Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Pauls, J., Schölkopf, B. & Logothetis, N.. (2005). Kernel Constrained Covariance for Dependence Measurement. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R5:112-119 Available from https://proceedings.mlr.press/r5/gretton05a.html. Reissued by PMLR on 30 March 2021.