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» Comparing distributions and shapes using the kernel distance
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96
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IPPS
2008
IEEE
15 years 7 months ago
Adaptive Locality-Effective Kernel Machine for protein phosphorylation site prediction
In this study, we propose a new machine learning model namely, Adaptive Locality-Effective Kernel Machine (Adaptive-LEKM) for protein phosphorylation site prediction. Adaptive-LEK...
Paul D. Yoo, Yung Shwen Ho, Bing Bing Zhou, Albert...
PAMI
2006
156views more  PAMI 2006»
15 years 14 days ago
Robust Point Matching for Nonrigid Shapes by Preserving Local Neighborhood Structures
In previous work on point matching, a set of points is often treated as an instance of a joint distribution to exploit global relationships in the point set. For nonrigid shapes, h...
Yefeng Zheng, David S. Doermann
126
Voted
CVPR
2004
IEEE
16 years 2 months ago
Learning Distance Functions for Image Retrieval
Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest to learn such distance functions by training binary ...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
112
Voted
CORR
2007
Springer
160views Education» more  CORR 2007»
15 years 16 days ago
On the Correlation of Geographic and Network Proximity at Internet Edges and its Implications for Mobile Unicast and Multicast R
Signicant eort has been invested recently to accelerate handover operations in a next generation mobile Internet. Corresponding works for developing ecient mobile multicast man...
Thomas C. Schmidt, Matthias Wählisch, Ying Zh...
204
Voted
ICDE
2002
IEEE
209views Database» more  ICDE 2002»
16 years 1 months ago
Geometric-Similarity Retrieval in Large Image Bases
We propose a novel approach to shape-based image retrieval that builds upon a similarity criterion which is based on the average point set distance. Compared to traditional techni...
Ioannis Fudos, Leonidas Palios, Evaggelia Pitoura