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Event 

Title:
Informatik-Kolloquium der GI: Multiple kernel learning and de-noising for similarity based classifie
When:
01.12.2011 - 01.12.2011 16.00 h
Where:
R3319, Ernst-Abbe-Platz 2 - Jena
Category:
Vorträge

Description

Multiple kernel learning and de-noising for similarity based classifiers

Josef Kittler, Centre for Vision, Speech and Signal Processing, University of Surrey Guildford, UK

 

Multiple kernel learning is becoming a popular method of boosting the performance of similarity based classifiers. It involves combining different kernels and finding the appropriate mixing parameters to achieve performance improvement. We focus on a multiple kernel learning (MKL) technique called lp-regularised multiple kernel Fisher discriminant analysis (MK-FDA), and investigate the effect of feature space de-noising on MKL. Experiments in image and video retrieval show that with both, the original kernels or de-noised kernels, lp MK-FDA outperforms its fixed-norm counterparts. Experiments also show that feature space de-noising boosts the performance of both single kernel FDA and lp MK-FDA. The methodology is applied to the problem of image database retrieval.

Venue

Venue:
R3319, Ernst-Abbe-Platz 2
Street:
Ernst-Abbe-Platz 2
ZIP:
07745
City:
Jena
Country:
Country: de

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