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» Making generative classifiers robust to selection bias
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KDD
2000
ACM
133views Data Mining» more  KDD 2000»
13 years 9 months ago
Data selection for support vector machine classifiers
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimiza...
Glenn Fung, Olvi L. Mangasarian
CP
2004
Springer
13 years 11 months ago
Generating Robust Partial Order Schedules
This paper considers the problem of transforming a resource feasible, fixed-times schedule into a partial order schedule (POS) to enhance its robustness and stability properties. ...
Nicola Policella, Angelo Oddi, Stephen F. Smith, A...
TRETS
2010
109views more  TRETS 2010»
13 years 28 days ago
Improving the Robustness of Ring Oscillator TRNGs
A ring oscillator based true-random number generator design (Rings design) was introduced in [1]. The design was rigorously analyzed under a mathematical model and its performance...
Sang-Kyung Yoo, Deniz Karakoyunlu, Berk Birand, Be...
PAMI
2006
136views more  PAMI 2006»
13 years 6 months ago
Data Driven Image Models through Continuous Joint Alignment
This paper presents a family of techniques that we call congealing for modeling image classes from data. The idea is to start with a set of images and make them appear as similar a...
Erik G. Learned-Miller
CVPR
2004
IEEE
13 years 10 months ago
Face Localization via Hierarchical CONDENSATION with Fisher Boosting Feature Selection
We formulate face localization as a Maximum A Posteriori Probability(MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribu...
Jilin Tu, ZhenQiu Zhang, Zhihong Zeng, Thomas S. H...