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ICML
2005
IEEE
16 years 13 days ago
New approaches to support vector ordinal regression
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal ...
Wei Chu, S. Sathiya Keerthi
IWANN
2005
Springer
15 years 5 months ago
Load Forecasting Using Fixed-Size Least Squares Support Vector Machines
Based on the Nystr¨om approximation and the primal-dual formulation of Least Squares Support Vector Machines (LS-SVM), it becomes possible to apply a nonlinear model to a large sc...
Marcelo Espinoza, Johan A. K. Suykens, Bart De Moo...
CDC
2010
IEEE
155views Control Systems» more  CDC 2010»
14 years 6 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
2001
IEEE
16 years 13 days 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
IJCNN
2006
IEEE
15 years 5 months ago
Pattern Selection for Support Vector Regression based on Sparseness and Variability
— Support Vector Machine has been well received in machine learning community with its theoretical as well as practical value. However, since its training time complexity is cubi...
Jiyoung Sun, Sungzoon Cho