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CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 5 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
DSP
2007
13 years 9 months ago
Blind separation of nonlinear mixtures by variational Bayesian learning
Blind separation of sources from nonlinear mixtures is a challenging and often ill-posed problem. We present three methods for solving this problem: an improved nonlinear factor a...
Antti Honkela, Harri Valpola, Alexander Ilin, Juha...
BIBM
2007
IEEE
14 years 4 months ago
A Semi-supervised Learning Approach to Disease Gene Prediction
Discovering human disease-causing genes (disease genes in short) is one of the most challenging problems in bioinformatics and biomedicine, as most diseases are related in some wa...
Thanh Phuong Nguyen, Tu Bao Ho
DATAMINE
2002
169views more  DATAMINE 2002»
13 years 9 months ago
Advances in Instance Selection for Instance-Based Learning Algorithms
The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time ...
Henry Brighton, Chris Mellish
GECCO
2006
Springer
140views Optimization» more  GECCO 2006»
14 years 1 months ago
A representational ecology for learning classifier systems
The representation used by a learning algorithm introduces a bias which is more or less well-suited to any given learning problem. It is well known that, across all possible probl...
James A. R. Marshall, Tim Kovacs