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» Generalization Bounds for Learning Kernels
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COMPGEOM
2011
ACM
13 years 2 days ago
A generic algebraic kernel for non-linear geometric applications
We report on a generic uni- and bivariate algebraic kernel that is publicly available with Cgal 3.7. It comprises complete, correct, though efficient state-of-the-art implementati...
Eric Berberich, Michael Hemmer, Michael Kerber
SAGA
2009
Springer
14 years 3 months ago
Bounds for Multistage Stochastic Programs Using Supervised Learning Strategies
We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible dec...
Boris Defourny, Damien Ernst, Louis Wehenkel
ICANN
2001
Springer
14 years 1 months ago
Incremental Support Vector Machine Learning: A Local Approach
Abstract. In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our meth...
Liva Ralaivola, Florence d'Alché-Buc
TCS
2010
13 years 6 months ago
Maximal width learning of binary functions
This paper concerns learning binary-valued functions defined on IR, and investigates how a particular type of ‘regularity’ of hypotheses can be used to obtain better generali...
Martin Anthony, Joel Ratsaby
ICDM
2008
IEEE
112views Data Mining» more  ICDM 2008»
14 years 3 months ago
Supervised Inductive Learning with Lotka-Volterra Derived Models
We present a classification algorithm built on our adaptation of the Generalized Lotka-Volterra model, well-known in mathematical ecology. The training algorithm itself consists ...
Karen Hovsepian, Peter Anselmo, Subhasish Mazumdar