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ECML
2006
Springer
15 years 2 months ago
Efficient Large Scale Linear Programming Support Vector Machines
This paper presents a decomposition method for efficiently constructing 1-norm Support Vector Machines (SVMs). The decomposition algorithm introduced in this paper possesses many d...
Suvrit Sra
101
Voted
SIGKDD
2000
139views more  SIGKDD 2000»
15 years 5 days ago
Support Vector Machines: Hype or Hallelujah?
Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. T...
Kristin P. Bennett, Colin Campbell
104
Voted
CDC
2009
IEEE
180views Control Systems» more  CDC 2009»
15 years 3 months ago
Robustness analysis for Least Squares kernel based regression: an optimization approach
—In kernel based regression techniques (such as Support Vector Machines or Least Squares Support Vector Machines) it is hard to analyze the influence of perturbed inputs on the ...
Tillmann Falck, Johan A. K. Suykens, Bart De Moor
ICANN
2001
Springer
15 years 4 months ago
Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We...
Thomas Frontzek, Thomas Navin Lal, Rolf Eckmiller
128
Voted
ISCI
2008
165views more  ISCI 2008»
15 years 12 days ago
Support vector regression from simulation data and few experimental samples
This paper considers nonlinear modeling based on a limited amount of experimental data and a simulator built from prior knowledge. The problem of how to best incorporate the data ...
Gérard Bloch, Fabien Lauer, Guillaume Colin...