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ICML
2001
IEEE
16 years 3 months ago
A Unified Loss Function in Bayesian Framework for Support Vector Regression
In this paper, we propose a unified non-quadratic loss function for regression known as soft insensitive loss function (SILF). SILF is a flexible model and possesses most of the d...
Wei Chu, S. Sathiya Keerthi, Chong Jin Ong
ECML
2005
Springer
15 years 8 months ago
Rotational Prior Knowledge for SVMs
Incorporation of prior knowledge into the learning process can significantly improve low-sample classification accuracy. We show how to introduce prior knowledge into linear supp...
Arkady Epshteyn, Gerald DeJong
IJCNN
2000
IEEE
15 years 7 months ago
A Neural Support Vector Network Architecture with Adaptive Kernels
In the Support Vector Machines (SVM) framework, the positive-definite kernel can be seen as representing a fixed similarity measure between two patterns, and a discriminant func...
Pascal Vincent, Yoshua Bengio
ICML
2000
IEEE
16 years 3 months ago
Bounds on the Generalization Performance of Kernel Machine Ensembles
We study the problem of learning using combinations of machines. In particular we present new theoretical bounds on the generalization performance of voting ensembles of kernel ma...
Luis Pérez-Breva, Massimiliano Pontil, Theo...
MICCAI
2006
Springer
16 years 3 months ago
The Entire Regularization Path for the Support Vector Domain Description
Abstract. The support vector domain description is a one-class classification method that estimates the shape and extent of the distribution of a data set. This separates the data ...
Karl Sjöstrand, Rasmus Larsen