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» Kernels for Multi--task Learning
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ICML
2007
IEEE
14 years 8 months ago
Gradient boosting for kernelized output spaces
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...
ICML
2004
IEEE
14 years 8 months ago
Multi-task feature and kernel selection for SVMs
We compute a common feature selection or kernel selection configuration for multiple support vector machines (SVMs) trained on different yet inter-related datasets. The method is ...
Tony Jebara
ICPR
2008
IEEE
14 years 2 months ago
Graph-based semi-supervised learning with redundant views
In this paper, we propose a novel semi-supervised algorithm, which works under a two-view setting. Our algorithm, named Kernel Canonical Component Analysis Graph (KC-GRAPH), can e...
Yun-Chao Gong, Chuanliang Chen, Yingjie Tian
IJCNN
2006
IEEE
14 years 1 months ago
Learning to Rank by Maximizing AUC with Linear Programming
— Area Under the ROC Curve (AUC) is often used to evaluate ranking performance in binary classification problems. Several researchers have approached AUC optimization by approxi...
Kaan Ataman, W. Nick Street, Yi Zhang
CORR
2008
Springer
100views Education» more  CORR 2008»
13 years 7 months ago
Learning Isometric Separation Maps
Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensionality spaces, often revealing the true intrinsic dimensio...
Nikolaos Vasiloglou, Alexander G. Gray, David V. A...