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ML
2002
ACM
178views Machine Learning» more  ML 2002»
13 years 7 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
ICMLC
2005
Springer
14 years 1 months ago
Kernel-Based Metric Adaptation with Pairwise Constraints
Abstract. Many supervised and unsupervised learning algorithms depend on the choice of an appropriate distance metric. While metric learning for supervised learning tasks has a lon...
Hong Chang, Dit-Yan Yeung
KDD
2012
ACM
254views Data Mining» more  KDD 2012»
11 years 10 months ago
Playlist prediction via metric embedding
Digital storage of personal music collections and cloud-based music services (e.g. Pandora, Spotify) have fundamentally changed how music is consumed. In particular, automatically...
Shuo Chen, Josh L. Moore, Douglas Turnbull, Thorst...
CVPR
2009
IEEE
13 years 5 months ago
Learning IMED via shift-invariant transformation
The IMage Euclidean Distance (IMED) is a class of image metrics, in which the spatial relationship between pixels is taken into consideration. It was shown that calculating the IM...
Bing Sun, Jufu Feng, Liwei Wang
COLT
2010
Springer
13 years 5 months ago
Efficient Classification for Metric Data
Recent advances in large-margin classification of data residing in general metric spaces (rather than Hilbert spaces) enable classification under various natural metrics, such as ...
Lee-Ad Gottlieb, Leonid Kontorovich, Robert Krauth...