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KAIS
2008
99views more  KAIS 2008»
13 years 7 months ago
Privacy-preserving SVM classification
Classical data mining algorithms implicitly assume complete access to all data, either in centralized or federated form. However, privacy and security concerns often prevent sharin...
Jaideep Vaidya, Hwanjo Yu, Xiaoqian Jiang
PAMI
2010
132views more  PAMI 2010»
13 years 6 months ago
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
—Adapting the hyperparameters of support vector machines (SVMs) is a challenging model selection problem, especially when flexible kernels are to be adapted and data are scarce....
Tobias Glasmachers, Christian Igel
ICML
2004
IEEE
14 years 8 months ago
Improving SVM accuracy by training on auxiliary data sources
The standard model of supervised learning assumes that training and test data are drawn from the same underlying distribution. This paper explores an application in which a second...
Pengcheng Wu, Thomas G. Dietterich
COLT
2003
Springer
14 years 24 days ago
Maximum Margin Algorithms with Boolean Kernels
Recent work has introduced Boolean kernels with which one can learn linear threshold functions over a feature space containing all conjunctions of length up to k (for any 1 ≤ k ...
Roni Khardon, Rocco A. Servedio
TASLP
2010
105views more  TASLP 2010»
13 years 2 months ago
Non-Negative Tensor Factorization Applied to Music Genre Classification
Music genre classification techniques are typically applied to the data matrix whose columns are the feature vectors extracted from music recordings. In this paper, a feature vecto...
Emmanouil Benetos, Constantine Kotropoulos