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» Spectral Algorithms for Supervised Learning
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COLT
2010
Springer
14 years 7 months ago
Learning Rotations with Little Regret
We describe online algorithms for learning a rotation from pairs of unit vectors in Rn . We show that the expected regret of our online algorithm compared to the best fixed rotati...
Elad Hazan, Satyen Kale, Manfred K. Warmuth
ICDM
2007
IEEE
129views Data Mining» more  ICDM 2007»
15 years 4 months ago
Semi-supervised Clustering Using Bayesian Regularization
Text clustering is most commonly treated as a fully automated task without user supervision. However, we can improve clustering performance using supervision in the form of pairwi...
Zuobing Xu, Ram Akella, Mike Ching, Renjie Tang
TSP
2012
13 years 5 months ago
Parametrization of Linear Systems Using Diffusion Kernels
—Modeling natural and artificial systems has played a key role in various applications and has long been a task that has drawn enormous efforts. In this work, instead of explori...
Ronen Talmon, Dan Kushnir, Ronald R. Coifman, Isra...
ICPR
2008
IEEE
15 years 11 months ago
Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach
We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...
Matthew T. Karnick, Michael Muhlbaier, Robi Polika...
PAMI
2010
276views more  PAMI 2010»
14 years 8 months ago
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
—This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature selection algorithm that addres...
Yijun Sun, Sinisa Todorovic, Steve Goodison