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...
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 ...
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 ...
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...
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...