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» Robust bounds for classification via selective sampling
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ICANN
2007
Springer
13 years 11 months ago
Resilient Approximation of Kernel Classifiers
Abstract. Trained support vector machines (SVMs) have a slow runtime classification speed if the classification problem is noisy and the sample data set is large. Approximating the...
Thorsten Suttorp, Christian Igel
CVPR
2010
IEEE
14 years 25 days ago
Visual Tracking via Weakly Supervised Learning from Multiple Imperfect Oracles
Long-term persistent tracking in ever-changing environments is a challenging task, which often requires addressing difficult object appearance update problems. To solve them, most...
Bineng Zhong, Hongxun Yao, Sheng Chen, Xiaotong Yu...
BIOINFORMATICS
2006
92views more  BIOINFORMATICS 2006»
13 years 7 months ago
What should be expected from feature selection in small-sample settings
Motivation: High-throughput technologies for rapid measurement of vast numbers of biological variables offer the potential for highly discriminatory diagnosis and prognosis; howev...
Chao Sima, Edward R. Dougherty
ICCV
2005
IEEE
14 years 9 months ago
Creating Efficient Codebooks for Visual Recognition
Visual codebook based quantization of robust appearance descriptors extracted from local image patches is an effective means of capturing image statistics for texture analysis and...
Bill Triggs, Frédéric Jurie
MICCAI
2003
Springer
14 years 8 months ago
An Automatic System for Classification of Nuclear Sclerosis from Slit-Lamp Photographs
A robust and automatic system has been developed to detect the visual axis and extract important feature landmarks from slit-lamp photographs, and objectively grade the severity of...
Shaohua Fan, Charles R. Dyer, Larry Hubbard, Barba...