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» Prediction on Spike Data Using Kernel Algorithms
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JMLR
2012
11 years 10 months ago
Metric and Kernel Learning Using a Linear Transformation
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
Prateek Jain, Brian Kulis, Jason V. Davis, Inderji...
RIVF
2008
13 years 9 months ago
Simple but effective methods for combining kernels in computational biology
Complex biological data generated from various experiments are stored in diverse data types in multiple datasets. By appropriately representing each biological dataset as a kernel ...
Hiroaki Tanabe, Tu Bao Ho, Canh Hao Nguyen, Saori ...
ESANN
2008
13 years 9 months ago
GeoKernels: modeling of spatial data on geomanifolds
This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental ...
Alexei Pozdnoukhov, Mikhail F. Kanevski
COGSCI
2008
90views more  COGSCI 2008»
13 years 7 months ago
Using fMRI to Test Models of Complex Cognition
This article investigates the potential of fMRI to test assumptions about different components in models of complex cognitive tasks. If the components of a model can be associated...
John R. Anderson, Cameron S. Carter, Jon M. Fincha...
BMCBI
2007
157views more  BMCBI 2007»
13 years 7 months ago
Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational
Background: High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination wi...
Nico Pfeifer, Andreas Leinenbach, Christian G. Hub...