Kernel Subspace Learning for Pattern Classification
Book chapter, 2018

How to use kernel methods in practice.
How to control the model complexity.
Kernel approximation as a subspace learning technique for feature construction.
Learning criteria for adaptive kernel approximation.
Infrastructure for kernel methods.

kernel methods

subspace learning

machine learning

Author

Yinan Yu

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Konstantinos I. Diamantaras

Tomas McKelvey

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

S. Y. Kung

Adaptive Learning Methods for Nonlinear System Modeling

127-147

Areas of Advance

Information and Communication Technology

Subject Categories

Computational Mathematics

Signal Processing

Computer Science

Roots

Basic sciences

DOI

10.1016/B978-0-12-812976-0.00021-X

More information

Created

7/2/2018 8