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» Nonlinear functional regression: a functional RKHS approach
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778views
17 years 4 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
DATE
2010
IEEE
185views Hardware» more  DATE 2010»
15 years 11 months ago
Fault diagnosis of analog circuits based on machine learning
— We discuss a fault diagnosis scheme for analog integrated circuits. Our approach is based on an assemblage of learning machines that are trained beforehand to guide us through ...
Ke Huang, Haralampos-G. D. Stratigopoulos, Salvado...
GECCO
2010
Springer
184views Optimization» more  GECCO 2010»
15 years 9 months ago
A mono surrogate for multiobjective optimization
Most surrogate approaches to multi-objective optimization build a surrogate model for each objective. These surrogates can be used inside a classical Evolutionary Multiobjective O...
Ilya Loshchilov, Marc Schoenauer, Michèle S...
IJCNN
2007
IEEE
16 years 15 days ago
Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines
Abstract— The optimal model parameters of a kernel machine are typically given by the solution of a convex optimisation problem with a single global optimum. Obtaining the best p...
Gavin C. Cawley, Nicola L. C. Talbot
CVPR
2005
IEEE
16 years 8 months ago
Fields of Experts: A Framework for Learning Image Priors
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The appro...
Stefan Roth, Michael J. Black