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130
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ICML
2007
IEEE
16 years 4 months ago
Information-theoretic metric learning
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative e...
Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit ...
129
Voted
ICML
2006
IEEE
16 years 4 months ago
Nonstationary kernel combination
The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several m...
Darrin P. Lewis, Tony Jebara, William Stafford Nob...
159
Voted
ICML
2006
IEEE
16 years 4 months ago
Cost-sensitive learning with conditional Markov networks
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
Prithviraj Sen, Lise Getoor
135
Voted
STOC
2006
ACM
116views Algorithms» more  STOC 2006»
16 years 4 months ago
Linear degree extractors and the inapproximability of max clique and chromatic number
: We derandomize results of H?astad (1999) and Feige and Kilian (1998) and show that for all > 0, approximating MAX CLIQUE and CHROMATIC NUMBER to within n1are NP-hard. We furt...
David Zuckerman
DCC
2004
IEEE
16 years 3 months ago
Fast Near-Lossless or Lossless Compression of Large 3D Neuro-Anatomical Images
3D neuro-anatomical images and other volumetric data sets are important in many scientific and biomedical fields. Since such sets may be extremely large, a scalable compression me...
Rongkai Zhao, Michael Gabriel, Geneva G. Belford