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» Support Kernel Machines for Object Recognition
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SDM
2009
SIAM
161views Data Mining» more  SDM 2009»
15 years 6 months ago
Feature Weighted SVMs Using Receiver Operating Characteristics.
Support Vector Machines (SVMs) are a leading tool in classification and pattern recognition and the kernel function is one of its most important components. This function is used...
Shaoyi Zhang, M. Maruf Hossain, Md. Rafiul Hassan,...
CVPR
2009
IEEE
1096views Computer Vision» more  CVPR 2009»
16 years 4 months ago
How far can you get with a modern face recognition test set using only simple features?
In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously ...
Nicolas Pinto, James J. DiCarlo, David D. Cox
ICIP
2004
IEEE
15 years 11 months ago
A robust face detector under partial occlusion
This paper presents a robust face detector under partial occlusion. In recent years, the effectiveness of Support Vector Machine (SVM) to object detection is reported. However, co...
Kazuhiro Hotta
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
15 years 1 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
CVPR
2010
IEEE
15 years 2 months ago
An Efficient Divide-and-Conquer Cascade for Nonlinear Object Detection
We introduce a method to accelerate the evaluation of object detection cascades with the help of a divide-andconquer procedure in the space of candidate regions. Compared to the e...
Christoph Lampert