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GECCO
2009
Springer
188views Optimization» more  GECCO 2009»
14 years 2 months ago
Exploiting multiple classifier types with active learning
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
Zhenyu Lu, Josh Bongard
PAMI
2012
12 years 1 months ago
Domain Transfer Multiple Kernel Learning
—Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has ...
Lixin Duan, Ivor W. Tsang, Dong Xu
FC
2009
Springer
67views Cryptology» more  FC 2009»
14 years 2 months ago
Privacy-Preserving Classifier Learning
We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database h...
Justin Brickell, Vitaly Shmatikov
NIPS
2004
14 years 9 days ago
Boosting on Manifolds: Adaptive Regularization of Base Classifiers
In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incorporating knowledge on the structure of the dat...
Balázs Kégl, Ligen Wang
ECML
1998
Springer
14 years 3 months ago
Learning to Classify X-Ray Images Using Relational Learning
: Image understanding often requires extensive background knowledge. The problem addressed in this paper is such knowledge can be acquired. We discuss how relational machine learni...
Claude Sammut, Tatjana Zrimec