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» Invariances in kernel methods: From samples to objects
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ML
2010
ACM
181views Machine Learning» more  ML 2010»
15 years 1 months ago
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels
Abstract The tensor kernel has been used across the machine learning literature for a number of purposes and applications, due to its ability to incorporate samples from multiple s...
David R. Hardoon, John Shawe-Taylor
CVPR
2011
IEEE
14 years 11 months ago
Scale and Rotation Invariant Matching Using Linearly Augmented Trees
We propose a novel linearly augmented tree method for efficient scale and rotation invariant object matching. The proposed method enforces pairwise matching consistency defined ...
Hao Jiang, Tai-Peng Tian, Stan Sclaroff
ICCV
1999
IEEE
15 years 7 months ago
Illumination Distribution from Brightness in Shadows: Adaptive Estimation of Illumination Distribution with Unknown Reflectance
This paper describes a new method for estimating the illumination distribution of a real scene from a radiance distribution inside shadows cast by an object in the scene. First, t...
Imari Sato, Yoichi Sato, Katsushi Ikeuchi
CVPR
2006
IEEE
16 years 5 months ago
Dimensionality Reduction by Learning an Invariant Mapping
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that "similar" points in input space are mapped to ne...
Raia Hadsell, Sumit Chopra, Yann LeCun
NIPS
2008
15 years 4 months ago
Kernel Change-point Analysis
We introduce a kernel-based method for change-point analysis within a sequence of temporal observations. Change-point analysis of an unlabelled sample of observations consists in,...
Zaïd Harchaoui, Francis Bach, Eric Moulines