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» Feature selection in a kernel space
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CVPR
2011
IEEE
13 years 5 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
ICCV
2009
IEEE
13 years 7 months ago
Selection and context for action recognition
Recognizing human action in non-instrumented video is a challenging task not only because of the variability produced by general scene factors like illumination, background, occlu...
Dong Han, Liefeng Bo, Cristian Sminchisescu
AAAI
2006
13 years 11 months ago
Unsupervised Order-Preserving Regression Kernel for Sequence Analysis
In this work, a generalized method for learning from sequence of unlabelled data points based on unsupervised order-preserving regression is proposed. Sequence learning is a funda...
Young-In Shin
ICASSP
2011
IEEE
13 years 1 months ago
A kernelized maximal-figure-of-merit learning approach based on subspace distance minimization
We propose a kernelized maximal-figure-of-merit (MFoM) learning approach to efficiently training a nonlinear model using subspace distance minimization. In particular, a fixed,...
Byungki Byun, Chin-Hui Lee
MICCAI
2005
Springer
14 years 11 months ago
Support Vector Clustering for Brain Activation Detection
In this paper, we propose a new approach to detect activated time series in functional MRI using support vector clustering (SVC). We extract Fourier coefficients as the features of...
Defeng Wang, Lin Shi, Daniel S. Yeung, Pheng-Ann H...