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» Evaluating learning algorithms and classifiers
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77
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ICCV
2009
IEEE
1019views Computer Vision» more  ICCV 2009»
16 years 5 months ago
Similarity Functions for Categorization: from Monolithic to Category Specific
Similarity metrics that are learned from labeled training data can be advantageous in terms of performance and/or efficiency. These learned metrics can then be used in conjuncti...
Boris Babenko, Steve Branson, Serge Belongie
113
Voted
IJCNN
2000
IEEE
15 years 4 months ago
Unsupervised Classification of Complex Clusters in Networks of Spiking Neurons
For unsupervised clustering in a network of spiking neurons we develop a temporal encoding of continuously valued data to obtain arbitrary clustering capacity and precision with a...
Sander M. Bohte, Johannes A. La Poutré, Joo...
PPAM
2005
Springer
15 years 6 months ago
A Web Computing Environment for Parallel Algorithms in Java
We present a web computing library (PUBWCL) in Java that allows to execute tightly coupled, massively parallel algorithms in the bulk-synchronous (BSP) style on PCs distributed ove...
Olaf Bonorden, Joachim Gehweiler, Friedhelm Meyer ...
JMLR
2010
113views more  JMLR 2010»
14 years 7 months ago
Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure
We study the problem of learning an optimal Bayesian network in a constrained search space; skeletons are compelled to be subgraphs of a given undirected graph called the super-st...
Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru M...
120
Voted
ICDM
2006
IEEE
119views Data Mining» more  ICDM 2006»
15 years 6 months ago
Fast On-line Kernel Learning for Trees
Kernel methods have been shown to be very effective for applications requiring the modeling of structured objects. However kernels for structures usually are too computational dem...
Fabio Aiolli, Giovanni Da San Martino, Alessandro ...