130 search results - page 4 / 26 » Kernel Machine Based Feature Extraction Algorithms for Regre... |
131
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IJON
2007
15 years 2 months ago
2007
This paper addresses the problem of variable ranking for Support Vector Regression. The relevance criteria that we proposed are based on leave-one-out bounds and some variants and...
112
Voted
ICML
2004
IEEE
16 years 3 months ago
2004
IEEE
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
133
Voted
ICML
2007
IEEE
16 years 3 months ago
2007
IEEE
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
114
Voted
ICCV
2009
IEEE
16 years 7 months ago
2009
IEEE
With the recent efforts made by computer vision researchers,
more and more types of features have been designed
to describe various aspects of visual characteristics.
Modeling s...
142
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CVPR
2010
IEEE
15 years 2 months ago
2010
IEEE
Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision p...