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» Algorithmic Complexity Bounds on Future Prediction Errors
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ICML
2008
IEEE
16 years 17 days ago
An RKHS for multi-view learning and manifold co-regularization
Inspired by co-training, many multi-view semi-supervised kernel methods implement the following idea: find a function in each of multiple Reproducing Kernel Hilbert Spaces (RKHSs)...
Vikas Sindhwani, David S. Rosenberg
NIPS
1998
15 years 1 months ago
Dynamically Adapting Kernels in Support Vector Machines
The kernel-parameter is one of the few tunable parameters in Support Vector machines, controlling the complexity of the resulting hypothesis. Its choice amounts to model selection...
Nello Cristianini, Colin Campbell, John Shawe-Tayl...
ICASSP
2011
IEEE
14 years 3 months ago
Error-entropy based channel state estimation of spatially correlated MIMO-OFDM
—This paper deals with optimized training sequences to estimate multiple-input multiple-output orthogonal frequencydivision multiplexing (MIMO-OFDM) channel states in the presenc...
Hoang Duong Tuan, Ha Hoang Kha, Ha H. Nguyen
CVPR
2005
IEEE
16 years 1 months ago
Modeling and Learning Contact Dynamics in Human Motion
We propose a simple model of human motion as a switching linear dynamical system where the switches correspond to contact forces with the ground. This significantly improves the m...
Alessandro Bissacco
PKDD
2009
Springer
147views Data Mining» more  PKDD 2009»
15 years 6 months ago
Kernel Polytope Faces Pursuit
Abstract. Polytope Faces Pursuit (PFP) is a greedy algorithm that approximates the sparse solutions recovered by 1 regularised least-squares (Lasso) [4,10] in a similar vein to (Or...
Tom Diethe, Zakria Hussain