Indefinite Kernels in Least Squares Support Vector Machines and Principal Component Analysis

Huang X, Maier A, Hornegger J, Suykens JAK (2016)

Publication Type: Journal article

Publication year: 2016

Journal

Applied and Computational Harmonic Analysis Elsevier

Journal Issue: 0

URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2016/Huang16-IKI.pdf

DOI: 10.1016/j.acha.2016.09.001

Authors with CRIS profile

Xiaolin Huang Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Joachim Hornegger Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)

Involved external institutions

Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven BE Belgium (BE)

How to cite

APA:

Huang, X., Maier, A., Hornegger, J., & Suykens, J.A.K. (2016). Indefinite Kernels in Least Squares Support Vector Machines and Principal Component Analysis. Applied and Computational Harmonic Analysis, 0. https://doi.org/10.1016/j.acha.2016.09.001

MLA:

Huang, Xiaolin, et al. "Indefinite Kernels in Least Squares Support Vector Machines and Principal Component Analysis." Applied and Computational Harmonic Analysis 0 (2016).

BibTeX: Download