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CDC
2009
IEEE
117views Control Systems» more  CDC 2009»
14 years 11 days ago
Risk sensitive robust support vector machines
— We propose a new family of classification algorithms in the spirit of support vector machines, that builds in non-conservative protection to noise and controls overfitting. O...
Huan Xu, Constantine Caramanis, Shie Mannor, Sungh...
AIR
2005
122views more  AIR 2005»
13 years 7 months ago
The Genetic Kernel Support Vector Machine: Description and Evaluation
The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kerne...
Tom Howley, Michael G. Madden
CDC
2010
IEEE
155views Control Systems» more  CDC 2010»
13 years 2 months ago
Linear parametric noise models for Least Squares Support Vector Machines
In the identification of nonlinear dynamical models it may happen that not only the system dynamics have to be modeled but also the noise has a dynamic character. We show how to ad...
Tillmann Falck, Johan A. K. Suykens, Bart De Moor
ICML
2005
IEEE
14 years 8 months ago
Supervised clustering with support vector machines
Supervised clustering is the problem of training a clustering algorithm to produce desirable clusterings: given sets of items and complete clusterings over these sets, we learn ho...
Thomas Finley, Thorsten Joachims
KDD
2006
ACM
181views Data Mining» more  KDD 2006»
14 years 8 months ago
Cryptographically private support vector machines
We study the problem of private classification using kernel methods. More specifically, we propose private protocols implementing the Kernel Adatron and Kernel Perceptron learning ...
Helger Lipmaa, Sven Laur, Taneli Mielikäinen