Kernel Methods Introduction to SVMs, KPCA, RDE
Lecture by Klaus- ̈ller, TUB 2021
Basic ideas in learning theory
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Klaus- ̈ller Lecture at TUB 2021
Klaus- ̈ller Lecture at TUB 2021
Basic ideas in learning theory II
Klaus- ̈ller Lecture at TUB 2021
Structural Risk Minimization: the picture
VC Dimensions: an examples
Klaus- ̈ller Lecture at TUB 2021
Linear Hyperplane Classifier
Klaus- ̈ller Lecture at TUB 2021
VC Theory applied to hyperplane classifiers
Klaus- ̈ller Lecture at TUB 2021
Feature Spaces & curse of dimensionality
Klaus- ̈ller Lecture at TUB 2021
Margin Distributions – large margin hyperplanes
Klaus- ̈ller Lecture at TUB 2021
Feature Spaces & curse of dimensionality
Klaus- ̈ller Lecture at TUB 2021
Nonlinear Algorithms in Feature Space
Klaus- ̈ller Lecture at TUB 2021
The kernel trick: an example
Klaus- ̈ller Lecture at TUB 2021
Klaus- ̈ller Lecture at TUB 2021
Kernology II
Klaus- ̈ller Lecture at TUB 2021
Klaus- ̈ller Lecture at TUB 2021
Klaus- ̈ller Lecture at TUB 2021
Klaus- ̈ller Lecture at TUB 2021
Klaus- ̈ller Lecture at TUB 2021
Dual Problem
Klaus- ̈ller Lecture at TUB 2021
Klaus- ̈ller Lecture at TUB 2021
Kernel Trick
Klaus- ̈ller Lecture at TUB 2021
Support Vector Machines in a nutshell
rsp. K(x,y) = (x) (y)
good theory
non-linear decision by implicitely mapping the data
into feature space by SV kernel function K
Klaus- ̈ller Lecture at TUB 2021
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