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» Learning Patterns in Noisy Data: The AQ Approach
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ICCV
2009
IEEE
1019views Computer Vision» more  ICCV 2009»
15 years 1 months ago
Similarity Functions for Categorization: from Monolithic to Category Specific
Similarity metrics that are learned from labeled training data can be advantageous in terms of performance and/or efficiency. These learned metrics can then be used in conjuncti...
Boris Babenko, Steve Branson, Serge Belongie
JCDL
2006
ACM
151views Education» more  JCDL 2006»
14 years 2 months ago
Tagging of name records for genealogical data browsing
In this paper we present a method of parsing unstructured textual records briefly describing a person and their direct relatives, which we use in the construction of a browsing t...
Mike Perrow, David Barber
PR
2008
123views more  PR 2008»
13 years 8 months ago
Extensions of vector quantization for incremental clustering
In this paper, we extend the conventional vector quantization by incorporating a vigilance parameter, which steers the tradeoff between plasticity and stability during incremental...
Edwin Lughofer
AMFG
2005
IEEE
203views Biometrics» more  AMFG 2005»
14 years 2 months ago
Learning to Fuse 3D+2D Based Face Recognition at Both Feature and Decision Levels
2D intensity images and 3D shape models are both useful for face recognition, but in different ways. While algorithms have long been developed using 2D or 3D data, recently has see...
Stan Z. Li, ChunShui Zhao, Meng Ao, Zhen Lei
JUCS
2006
185views more  JUCS 2006»
13 years 8 months ago
The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States
We outline the Berlin Brain-Computer Interface (BBCI), a system which enables us to translate brain signals from movements or movement intentions into control commands. The main co...
Benjamin Blankertz, Guido Dornhege, Steven Lemm, M...