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» Invariances in kernel methods: From samples to objects
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PAKDD
2007
ACM
110views Data Mining» more  PAKDD 2007»
14 years 1 months ago
Combining Convolution Kernels Defined on Heterogeneous Sub-structures
Convolution kernels, constructed by convolution of sub-kernels defined on sub-structures of composite objects, are widely used in classification, where one important issue is to ch...
Minlie Huang, Xiaoyan Zhu
BMCBI
2008
228views more  BMCBI 2008»
13 years 7 months ago
Adaptive diffusion kernel learning from biological networks for protein function prediction
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...
Liang Sun, Shuiwang Ji, Jieping Ye
TSP
2010
13 years 2 months ago
Sampling piecewise sinusoidal signals with finite rate of innovation methods
We consider the problem of sampling piecewise sinusoidal signals. Classical sampling theory does not enable perfect reconstruction of such signals since they are not bandlimited. ...
Jesse Berent, Pier Luigi Dragotti, Thierry Blu
BMVC
2000
13 years 9 months ago
Object Recognition using the Invariant Pixel-Set Signature
A new object recognition method, the Invariant Pixel Set Signature (IPSS), is introduced. Objects are represented with a probability density on the space of invariants computed fr...
Jiri Matas, J. Burianek, Josef Kittler
CVPR
2009
IEEE
1453views Computer Vision» more  CVPR 2009»
14 years 12 months ago
Learning Photometric Invariance From Diversified Color Model Ensembles
Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the i...
Jose M. Alvarez, Theo Gevers, Antonio M. Lopez