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PAMI
2006
147views more  PAMI 2006»
15 years 2 months ago
Bayesian Gaussian Process Classification with the EM-EP Algorithm
Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
Hyun-Chul Kim, Zoubin Ghahramani
149
Voted
PAMI
2010
188views more  PAMI 2010»
15 years 29 days ago
A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding
—Facial expression is a natural and powerful means of human communication. Recognizing spontaneous facial actions, however, is very challenging due to subtle facial deformation, ...
Yan Tong, Jixu Chen, Qiang Ji
IDEAL
2009
Springer
15 years 8 days ago
Optimizing Data Transformations for Classification Tasks
Many classification algorithms use the concept of distance or similarity between patterns. Previous work has shown that it is advantageous to optimize general Euclidean distances (...
José María Valls, Ricardo Aler
151
Voted
JMLR
2010
125views more  JMLR 2010»
14 years 9 months ago
On utility of gene set signatures in gene expression-based cancer class prediction
Machine learning methods that can use additional knowledge in their inference process are central to the development of integrative bioinformatics. Inclusion of background knowled...
Minca Mramor, Marko Toplak, Gregor Leban, Tomaz Cu...
ICASSP
2011
IEEE
14 years 6 months ago
Application specific loss minimization using gradient boosting
Gradient boosting is a flexible machine learning technique that produces accurate predictions by combining many weak learners. In this work, we investigate its use in two applica...
Bin Zhang, Abhinav Sethy, Tara N. Sainath, Bhuvana...