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JMLR
2008
141views more  JMLR 2008»
14 years 10 months ago
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
In graphical modelling, a bi-directed graph encodes marginal independences among random variables that are identified with the vertices of the graph. We show how to transform a bi...
Mathias Drton, Thomas S. Richardson
JMLR
2008
110views more  JMLR 2008»
14 years 10 months ago
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
Support vector machine (SVM) is one of the most popular and promising classification algorithms. After a classification rule is constructed via the SVM, it is essential to evaluat...
Bo Jiang, Xuegong Zhang, Tianxi Cai
JMLR
2008
79views more  JMLR 2008»
14 years 10 months ago
Manifold Learning: The Price of Normalization
We analyze the performance of a class of manifold-learning algorithms that find their output by minimizing a quadratic form under some normalization constraints. This class consis...
Yair Goldberg, Alon Zakai, Dan Kushnir, Yaacov Rit...
JMLR
2008
188views more  JMLR 2008»
14 years 9 months ago
Maximal Causes for Non-linear Component Extraction
We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable thr...
Jörg Lücke, Maneesh Sahani
JMLR
2010
101views more  JMLR 2010»
14 years 4 months ago
Exploiting Feature Covariance in High-Dimensional Online Learning
Some online algorithms for linear classification model the uncertainty in their weights over the course of learning. Modeling the full covariance structure of the weights can prov...
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer...