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» Machine Learning by Function Decomposition
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JMLR
2010
123views more  JMLR 2010»
14 years 6 months ago
Inductive Principles for Restricted Boltzmann Machine Learning
Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with in...
Benjamin Marlin, Kevin Swersky, Bo Chen, Nando de ...
ML
2008
ACM
134views Machine Learning» more  ML 2008»
14 years 11 months ago
Multilabel classification via calibrated label ranking
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operat...
Johannes Fürnkranz, Eyke Hüllermeier, En...
ECTEL
2006
Springer
15 years 3 months ago
Community Based Software Development - the Case of Movelex
Abstract. The paper provides an overview of the elaboration, testing and improvement of Movelex, a complex virtual learning environment (VLE) supporting the establishment of self-r...
Kornél Varga, Andrea Kárpáti
ICML
2006
IEEE
16 years 19 days ago
Learning a kernel function for classification with small training samples
When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. Thi...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
15 years 6 months ago
Defining implicit objective functions for design problems
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
Sean Hanna