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» Making generative classifiers robust to selection bias
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KDD
2000
ACM
133views Data Mining» more  KDD 2000»
14 years 1 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
14 years 3 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 5 months 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 10 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
14 years 2 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...