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PG
2003
IEEE
14 years 28 days ago
Machine Learning for Computer Graphics: A Manifesto and Tutorial
I argue that computer graphics can benefit from a deeper use of machine learning techniques. I give an overview of what learning has to offer the graphics community, with an emph...
Aaron Hertzmann
ICML
2007
IEEE
14 years 8 months ago
Transductive support vector machines for structured variables
We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over a...
Alexander Zien, Ulf Brefeld, Tobias Scheffer
ISMB
1993
13 years 9 months ago
Knowledge-Based Generation of Machine-Learning Experiments: Learning with DNA Crystallography Data
Thoughit has been possible in the past to learn to predict DNAhydration patterns from crystallographic data, there is ambiguity in the choice of training data (both in terms of th...
Dawn M. Cohen, Casimir A. Kulikowski, Helen Berman
ALT
2010
Springer
13 years 9 months ago
Bayesian Active Learning Using Arbitrary Binary Valued Queries
We explore a general Bayesian active learning setting, in which the learner can ask arbitrary yes/no questions. We derive upper and lower bounds on the expected number of queries r...
Liu Yang, Steve Hanneke, Jaime G. Carbonell
ICML
2006
IEEE
14 years 8 months ago
The support vector decomposition machine
In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learni...
Francisco Pereira, Geoffrey J. Gordon