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Input space versus feature space in kernel-based methods.
Bernhard Schölkopf
Sebastian Mika
Christopher J. C. Burges
Phil Knirsch
Klaus-Robert Müller
Gunnar Rätsch
Alexander J. Smola
Published in:
IEEE Trans. Neural Networks (1999)
Keyphrases
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input space
feature space
dimensionality reduction
high dimensional
high dimensional data
kernel trick
kernel pca
data points
low dimensional
kernel function
feature selection
neural network
principal component analysis
closely related
pattern recognition
hyperplane
receptive fields
support vector
high dimension