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TNN
2010
143views Management» more  TNN 2010»
15 years 1 months ago
Using unsupervised analysis to constrain generalization bounds for support vector classifiers
Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
Sergio Decherchi, Sandro Ridella, Rodolfo Zunino, ...
ICCV
2007
IEEE
16 years 8 months ago
Linear Predictors for Fast Simultaneous Modeling and Tracking
An approach for fast tracking of arbitrary image features with no prior model and no offline learning stage is presented. Fast tracking is achieved using banks of linear displacem...
Liam Ellis, Nicholas Dowson, Jiri Matas, Richard B...
ICML
2005
IEEE
16 years 7 months ago
Preference learning with Gaussian processes
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...
Wei Chu, Zoubin Ghahramani
HICSS
2010
IEEE
146views Biometrics» more  HICSS 2010»
16 years 1 months ago
Who's Right and Who Writes: People, Profiles, Contacts, and Replies in Online Dating
In this analysis of profiles and messaging behavior on a major online dating service, we find that, consistent with predictions of evolutionary psychology, women as compared to me...
Andrew T. Fiore, Lindsay Shaw Taylor, Xiaomeng Zho...
APGV
2005
ACM
132views Visualization» more  APGV 2005»
16 years 10 days ago
De-emphasis of distracting image regions using texture power maps
We present a post-processing technique that selectively reduces the salience of distracting regions in an image. Computational models of attention predict that texture variation i...
Sara L. Su, Frédo Durand, Maneesh Agrawala