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» Optimizing kernel parameters by second-order methods
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JMLR
2012
11 years 10 months ago
Krylov Subspace Descent for Deep Learning
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In ou...
Oriol Vinyals, Daniel Povey
PRL
2000
76views more  PRL 2000»
13 years 7 months ago
Road sign classification using Laplace kernel classifier
Driver support systems of intelligent vehicles will predict potentially dangerous situations in heavy traffic, help with navigation and vehicle guidance and interact with a human ...
Pavel Paclík, Jana Novovicová, Pavel...
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
13 years 11 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
NIPS
2007
13 years 9 months ago
Hierarchical Penalization
Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hiera...
Marie Szafranski, Yves Grandvalet, Pierre Morizet-...
ICRA
2006
IEEE
165views Robotics» more  ICRA 2006»
14 years 1 months ago
Biped Gait Optimization using Spline Function based Probability Model
— A new Estimation of Distribution Algorithm (EDA) with spline kernel function (EDA_S) is proposed to optimize biped gait for a nine-link humanoid robot. Gait synthesis of the bi...
Lingyun Hu, Changjiu Zhou, Zengqi Sun