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» A distributed machine learning framework
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115
Voted
ICML
2005
IEEE
16 years 3 months ago
Expectation maximization algorithms for conditional likelihoods
We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the dist...
Jarkko Salojärvi, Kai Puolamäki, Samuel ...
140
Voted
ICML
2003
IEEE
16 years 3 months ago
Regression Error Characteristic Curves
Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generaliz...
Jinbo Bi, Kristin P. Bennett
96
Voted
ICML
2003
IEEE
16 years 3 months ago
A Kernel Between Sets of Vectors
In various application domains, including image recognition, it is natural to represent each example as a set of vectors. With a base kernel we can implicitly map these vectors to...
Risi Imre Kondor, Tony Jebara
144
Voted
ICML
1999
IEEE
16 years 3 months ago
AdaCost: Misclassification Cost-Sensitive Boosting
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boo...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip...
102
Voted
ICML
2009
IEEE
15 years 9 months ago
Nonparametric estimation of the precision-recall curve
The Precision-Recall (PR) curve is a widely used visual tool to evaluate the performance of scoring functions in regards to their capacities to discriminate between two population...
Stéphan Clémençon, Nicolas Va...