ICML2011 Unsupervised and Transfer Learning Workshop

David L. Silver, Isabelle Guyon, Graham Taylor, Gideon Dror, Vincent Lemaire
Proceedings of ICML Workshop on Unsupervised and Transfer Learning, PMLR 27:1-15, 2012.

Abstract

We organized a data mining challenge in “unsupervised and transfer learning” (the UTL challenge) followed by a workshop of the same name at the ICML 2011 conference in Bellevue, Washington. This introduction presents the highlights of the outstanding contributions that were made, which are regrouped in this issue of JMLR W&CP. Novel methodologies emerged to capitalize on large volumes of unlabeled data from tasks related (but different) from a target task, including a method to learn data kernels (similarity measures) and new deep architectures for feature learning.

Cite this Paper

BibTeX
@InProceedings{pmlr-v27-silver12a, title = {ICML2011 Unsupervised and Transfer Learning Workshop}, author = {Silver, David L. and Guyon, Isabelle and Taylor, Graham and Dror, Gideon and Lemaire, Vincent}, booktitle = {Proceedings of ICML Workshop on Unsupervised and Transfer Learning}, pages = {1--15}, year = {2012}, editor = {Guyon, Isabelle and Dror, Gideon and Lemaire, Vincent and Taylor, Graham and Silver, Daniel}, volume = {27}, series = {Proceedings of Machine Learning Research}, address = {Bellevue, Washington, USA}, month = {02 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v27/silver12a/silver12a.pdf}, url = {https://proceedings.mlr.press/v27/silver12a.html}, abstract = {We organized a data mining challenge in “unsupervised and transfer learning” (the UTL challenge) followed by a workshop of the same name at the ICML 2011 conference in Bellevue, Washington. This introduction presents the highlights of the outstanding contributions that were made, which are regrouped in this issue of JMLR W&CP. Novel methodologies emerged to capitalize on large volumes of unlabeled data from tasks related (but different) from a target task, including a method to learn data kernels (similarity measures) and new deep architectures for feature learning.} }
Endnote
%0 Conference Paper %T ICML2011 Unsupervised and Transfer Learning Workshop %A David L. Silver %A Isabelle Guyon %A Graham Taylor %A Gideon Dror %A Vincent Lemaire %B Proceedings of ICML Workshop on Unsupervised and Transfer Learning %C Proceedings of Machine Learning Research %D 2012 %E Isabelle Guyon %E Gideon Dror %E Vincent Lemaire %E Graham Taylor %E Daniel Silver %F pmlr-v27-silver12a %I PMLR %P 1--15 %U https://proceedings.mlr.press/v27/silver12a.html %V 27 %X We organized a data mining challenge in “unsupervised and transfer learning” (the UTL challenge) followed by a workshop of the same name at the ICML 2011 conference in Bellevue, Washington. This introduction presents the highlights of the outstanding contributions that were made, which are regrouped in this issue of JMLR W&CP. Novel methodologies emerged to capitalize on large volumes of unlabeled data from tasks related (but different) from a target task, including a method to learn data kernels (similarity measures) and new deep architectures for feature learning.
RIS
TY - CPAPER TI - ICML2011 Unsupervised and Transfer Learning Workshop AU - David L. Silver AU - Isabelle Guyon AU - Graham Taylor AU - Gideon Dror AU - Vincent Lemaire BT - Proceedings of ICML Workshop on Unsupervised and Transfer Learning DA - 2012/06/27 ED - Isabelle Guyon ED - Gideon Dror ED - Vincent Lemaire ED - Graham Taylor ED - Daniel Silver ID - pmlr-v27-silver12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 27 SP - 1 EP - 15 L1 - http://proceedings.mlr.press/v27/silver12a/silver12a.pdf UR - https://proceedings.mlr.press/v27/silver12a.html AB - We organized a data mining challenge in “unsupervised and transfer learning” (the UTL challenge) followed by a workshop of the same name at the ICML 2011 conference in Bellevue, Washington. This introduction presents the highlights of the outstanding contributions that were made, which are regrouped in this issue of JMLR W&CP. Novel methodologies emerged to capitalize on large volumes of unlabeled data from tasks related (but different) from a target task, including a method to learn data kernels (similarity measures) and new deep architectures for feature learning. ER -
APA
Silver, D.L., Guyon, I., Taylor, G., Dror, G. & Lemaire, V.. (2012). ICML2011 Unsupervised and Transfer Learning Workshop. Proceedings of ICML Workshop on Unsupervised and Transfer Learning, in Proceedings of Machine Learning Research 27:1-15 Available from https://proceedings.mlr.press/v27/silver12a.html.

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