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IPL
2006
110views more  IPL 2006»
13 years 7 months ago
Variationally universal hashing
The strongest well-known measure for the quality of a universal hash-function family H is its being -strongly universal, which measures, for randomly chosen h H, one's inabi...
Ted Krovetz, Phillip Rogaway
SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
14 years 8 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...

Publication
922views
15 years 2 months ago
Multi-Class Active Learning for Image Classification
One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, ...
Ajay J. Joshi, Fatih Porikli, Nikolaos Papanikolop...
CVPR
2007
IEEE
14 years 10 months ago
Learning Visual Similarity Measures for Comparing Never Seen Objects
In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled ...
Eric Nowak, Frédéric Jurie
CORR
2008
Springer
129views Education» more  CORR 2008»
13 years 8 months ago
A polytime proof of correctness of the Rabin-Miller algorithm from Fermat's little theorem
Although a deterministic polytime algorithm for primality testing is now known ([4]), the Rabin-Miller randomized test of primality continues being the most efficient and widely u...
Grzegorz Herman, Michael Soltys