TY  - THES
U1  - Master Thesis
A1  - Stürmer, Paul
T1  - Hellinger divergence in information theoretic novelty detection
N2  - In this work a novelty detection framework provided by M. Filippone and G. Sanguinetti is considered, which is useful especially when only few training samples are available. It is restricted to Gaussian mixture models and makes use of information theory, applying the Kullback-Leibler divergence. In this work two variations of the framework are presented, applying the symmetric Hellinger divergence and a statistical likelihood approach.
KW  - Wahrscheinlichkeitsverteilung
Y2  - 2014
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:mit1-opus-46323
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mit1-opus-46323
ER  -