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» Sublinear Optimization for Machine Learning
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ICML
2010
IEEE
15 years 7 months ago
Risk minimization, probability elicitation, and cost-sensitive SVMs
A new procedure for learning cost-sensitive SVM classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the cost-sensitive SVM is derived as the...
Hamed Masnadi-Shirazi, Nuno Vasconcelos
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
15 years 10 months ago
On the appropriateness of evolutionary rule learning algorithms for malware detection
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classiï...
M. Zubair Shafiq, S. Momina Tabish, Muddassar Faro...
GECCO
2009
Springer
110views Optimization» more  GECCO 2009»
15 years 10 months ago
EMO shines a light on the holes of complexity space
Typical domains used in machine learning analyses only partially cover the complexity space, remaining a large proportion of problem difficulties that are not tested. Since the ac...
Núria Macià, Albert Orriols-Puig, Es...
DSMML
2004
Springer
15 years 11 months ago
Can Gaussian Process Regression Be Made Robust Against Model Mismatch?
Learning curves for Gaussian process (GP) regression can be strongly affected by a mismatch between the ‘student’ model and the ‘teacher’ (true data generation process), e...
Peter Sollich
ECML
2006
Springer
15 years 9 months ago
Task-Driven Discretization of the Joint Space of Visual Percepts and Continuous Actions
We target the problem of closed-loop learning of control policies that map visual percepts to continuous actions. Our algorithm, called Reinforcement Learning of Joint Classes (RLJ...
Sébastien Jodogne, Justus H. Piater