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» Sampling Techniques for Kernel Methods
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PAMI
2012
13 years 4 days ago
Domain Transfer Multiple Kernel Learning
—Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has ...
Lixin Duan, Ivor W. Tsang, Dong Xu
ICASSP
2010
IEEE
14 years 10 months ago
Near-field adaptive beamforming and source localization in the spacetime frequency domain
We revisit the topics of near-field adaptive beamforming and source localization following an alternative approach based on a spatiotemporal spectral representation of the acoust...
Francisco Pinto, Martin Vetterli
NIPS
2003
14 years 11 months ago
Sequential Bayesian Kernel Regression
We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the num...
Jaco Vermaak, Simon J. Godsill, Arnaud Doucet
ALGORITHMICA
2008
79views more  ALGORITHMICA 2008»
14 years 9 months ago
Practical Methods for Shape Fitting and Kinetic Data Structures using Coresets
The notion of -kernel was introduced by Agarwal et al. [5] to set up a unified framework for computing various extent measures of a point set P approximately. Roughly speaking, a ...
Hai Yu, Pankaj K. Agarwal, Raghunath Poreddy, Kast...
KDD
2006
ACM
180views Data Mining» more  KDD 2006»
15 years 10 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang