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» Relevance Vector Machine Analysis of Functional Neuroimages
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ICML
2007
IEEE
16 years 2 months ago
Regression on manifolds using kernel dimension reduction
We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads ...
Jens Nilsson, Fei Sha, Michael I. Jordan
ML
2010
ACM
185views Machine Learning» more  ML 2010»
14 years 8 months ago
Learning to rank on graphs
Graph representations of data are increasingly common. Such representations arise in a variety of applications, including computational biology, social network analysis, web applic...
Shivani Agarwal
MM
2004
ACM
167views Multimedia» more  MM 2004»
15 years 7 months ago
Learning an image manifold for retrieval
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded ...
Xiaofei He, Wei-Ying Ma, HongJiang Zhang
103
Voted
CIBCB
2005
IEEE
15 years 7 months ago
A Neural Network for Predicting Protein Disorder using Amino Acid Hydropathy Values
— Proteins have been discovered to contain ordered regions and disordered regions, where ordered regions have a defined three-dimensional (3D) structure and disordered regions d...
Deborah Stoffer, L. Gwenn Volkert
131
Voted
JMLR
2006
134views more  JMLR 2006»
15 years 1 months ago
Considering Cost Asymmetry in Learning Classifiers
Receiver Operating Characteristic (ROC) curves are a standard way to display the performance of a set of binary classifiers for all feasible ratios of the costs associated with fa...
Francis R. Bach, David Heckerman, Eric Horvitz