Title: Primal-dual monotone kernel regression
Authors: Pelckmans, Kristiaan ×
Espinoza, M
De Brabanter, Joseph
Suykens, Johan
De Moor, Bart #
Issue Date: Oct-2005
Publisher: D facto
Series Title: Neural Processing Letters vol:22 issue:2 pages:171-182
Abstract: This paper considers the estimation of monotone nonlinear regression functions based on Support Vector Machines (SVMs), Least Squares SVMs (LS-SVMs) and other kernel machines. It illustrates how to employ the primal-dual optimization framework characterizing LS-SVMs in order to derive a globally optimal one-stage estimator for monotone regression. As a practical application, this letter considers the smooth estimation of the cumulative distribution functions (cdf), which leads to a kernel regressor that incorporates a Kolmogorov-Smirnoff discrepancy measure, a Tikhonov based regularization scheme and a monotonicity constraint.
ISSN: 1370-4621
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Centre for Food and Microbial Technology
Electrical Engineering - miscellaneous
Technologiecluster ESAT Elektrotechnische Engineering
Electrical Engineering (ESAT) TC, Technology Campuses Ghent and Aalst
× corresponding author
# (joint) last author

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