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» Approximation algorithms for budgeted learning problems
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ICML
2007
IEEE
15 years 10 months ago
Supervised feature selection via dependence estimation
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The ...
Le Song, Alex J. Smola, Arthur Gretton, Karsten M....
111
Voted
ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
15 years 3 months ago
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
ML
2002
ACM
146views Machine Learning» more  ML 2002»
14 years 9 months ago
Kernel Matching Pursuit
Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target fu...
Pascal Vincent, Yoshua Bengio
ECCV
2006
Springer
15 years 11 months ago
Learning Nonlinear Manifolds from Time Series
Abstract. There has been growing interest in developing nonlinear dimensionality reduction algorithms for vision applications. Although progress has been made in recent years, conv...
Ruei-Sung Lin, Che-Bin Liu, Ming-Hsuan Yang, Naren...
97
Voted
SIGMOD
2011
ACM
276views Database» more  SIGMOD 2011»
14 years 15 days ago
Schedule optimization for data processing flows on the cloud
Scheduling data processing workflows (dataflows) on the cloud is a very complex and challenging task. It is essentially an optimization problem, very similar to query optimizati...
Herald Kllapi, Eva Sitaridi, Manolis M. Tsangaris,...