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» Learning from Multiple Annotators with Gaussian Processes
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BMCBI
2004
106views more  BMCBI 2004»
14 years 11 months ago
ESTIMA, a tool for EST management in a multi-project environment
Background: Single-pass, partial sequencing of complementary DNA (cDNA) libraries generates thousands of chromatograms that are processed into high quality expressed sequence tags...
Charu G. Kumar, Richard LeDuc, George Gong, Levan ...
ML
2002
ACM
220views Machine Learning» more  ML 2002»
14 years 11 months ago
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
Peter Sollich
ECCV
2002
Springer
16 years 1 months ago
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
We present a variational integration of nonlinear shape statistics into a Mumford?Shah based segmentation process. The nonlinear statistics are derived from a set of training silho...
Christoph Schnörr, Daniel Cremers, Timo Kohlb...
ICASSP
2011
IEEE
14 years 3 months ago
Use of VTL-wise models in feature-mapping framework to achieve performance of multiple-background models in speaker verification
Recently, Multiple Background Models (M-BMs) [1, 2] have been shown to be useful in speaker verification, where the M-BMs are formed based on different Vocal Tract Lengths (VTLs)...
Achintya Kumar Sarkar, Srinivasan Umesh
BMCBI
2008
101views more  BMCBI 2008»
14 years 12 months ago
Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in dros
Background: Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene O...
Wensheng Zhang, Sige Zou, Jiuzhou Song