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» Nonparametric comparison of regression functions
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GECCO
2007
Springer
166views Optimization» more  GECCO 2007»
14 years 1 months ago
Comparison of tree and graph encodings as function of problem complexity
In this paper, we analyze two general-purpose encoding types, trees and graphs systematically, focusing on trends over increasingly complex problems. Tree and graph encodings are ...
Michael D. Schmidt, Hod Lipson
ESANN
2006
13 years 8 months ago
Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks
Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regre...
Aloísio Carlos de Pina, Gerson Zaverucha
NIPS
2004
13 years 8 months ago
Learning Gaussian Process Kernels via Hierarchical Bayes
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...
Anton Schwaighofer, Volker Tresp, Kai Yu
IROS
2008
IEEE
191views Robotics» more  IROS 2008»
14 years 1 months ago
Local Gaussian process regression for real-time model-based robot control
— High performance and compliant robot control requires accurate dynamics models which cannot be obtained analytically for sufficiently complex robot systems. In such cases, mac...
Duy Nguyen-Tuong, Jan Peters
ICML
2010
IEEE
13 years 8 months ago
Budgeted Nonparametric Learning from Data Streams
We consider the problem of extracting informative exemplars from a data stream. Examples of this problem include exemplarbased clustering and nonparametric inference such as Gauss...
Ryan Gomes, Andreas Krause