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» Kernelization for Convex Recoloring
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ICANN
2005
Springer
14 years 26 days ago
The LCCP for Optimizing Kernel Parameters for SVM
Abstract. Tuning hyper-parameters is a necessary step to improve learning algorithm performances. For Support Vector Machine classifiers, adjusting kernel parameters increases dra...
Sabri Boughorbel, Jean-Philippe Tarel, Nozha Bouje...
ICML
2008
IEEE
14 years 8 months ago
Localized multiple kernel learning
Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is opt...
Ethem Alpaydin, Mehmet Gönen
TNN
2008
81views more  TNN 2008»
13 years 7 months ago
Nonlinear Knowledge-Based Classification
Prior knowledge over general nonlinear sets is incorporated into nonlinear kernel classification problems as linear constraints in a linear program. The key tool in this incorpora...
Olvi L. Mangasarian, Edward W. Wild
JMLR
2010
119views more  JMLR 2010»
13 years 2 months ago
Semi-Supervised Learning via Generalized Maximum Entropy
Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that max...
Ayse Erkan, Yasemin Altun
JMLR
2011
157views more  JMLR 2011»
13 years 2 months ago
Variable Sparsity Kernel Learning
This paper1 presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assu...
Jonathan Aflalo, Aharon Ben-Tal, Chiranjib Bhattac...