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KDD
2007
ACM
276views Data Mining» more  KDD 2007»
14 years 7 months ago
Nonlinear adaptive distance metric learning for clustering
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu
GBRPR
2009
Springer
14 years 1 months ago
On Computing Canonical Subsets of Graph-Based Behavioral Representations
The collection of behavior protocols is a common practice in human factors research, but the analysis of these large data sets has always been a tedious and time-consuming process....
Walter C. Mankowski, Peter Bogunovich, Ali Shokouf...
ICALP
2004
Springer
14 years 22 days ago
Linear and Branching Metrics for Quantitative Transition Systems
Abstract. We extend the basic system relations of trace inclusion, trace equivalence, simulation, and bisimulation to a quantitative setting in which propositions are interpreted n...
Luca de Alfaro, Marco Faella, Mariëlle Stoeli...
DAM
2008
116views more  DAM 2008»
13 years 7 months ago
Expected number of breakpoints after t random reversals in genomes with duplicate genes
In comparative genomics, one wishes to deduce the evolutionary distance between dierent species by studying their genomes. Using gene order information, we seek the number of time...
Niklas Eriksen
ICCV
2009
IEEE
13 years 5 months ago
Local distance functions: A taxonomy, new algorithms, and an evaluation
We present a taxonomy for local distance functions where most existing algorithms can be regarded as approximations of the geodesic distance defined by a metric tensor. We categor...
Deva Ramanan, Simon Baker