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ML
2008
ACM
162views Machine Learning» more  ML 2008»
15 years 10 days ago
Incorporating prior knowledge in support vector regression
This paper explores the addition of constraints to the linear programming formulation of the support vector regression problem for the incorporation of prior knowledge. Equality an...
Fabien Lauer, Gérard Bloch
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 7 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 1 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
127
Voted
INCDM
2010
Springer
308views Data Mining» more  INCDM 2010»
15 years 2 months ago
Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool
We present rminer, our open source library for the R tool that facilitates the use of data mining (DM) algorithms, such as neural Networks (NNs) and support vector machines (SVMs),...
Paulo Cortez