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» A Bayesian Metric for Evaluating Machine Learning Algorithms
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ICPR
2006
IEEE
14 years 8 months ago
Precision-recall operating characteristic (P-ROC) curves in imprecise environments
Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this pa...
Thomas Landgrebe, Pavel Paclík, Robert P. W...
KDD
2002
ACM
179views Data Mining» more  KDD 2002»
14 years 7 months ago
Combining clustering and co-training to enhance text classification using unlabelled data
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for...
Bhavani Raskutti, Herman L. Ferrá, Adam Kow...
CIA
2007
Springer
14 years 1 months ago
Quantifying the Expected Utility of Information in Multi-agent Scheduling Tasks
Abstract. In this paper we investigate methods for analyzing the expected value of adding information in distributed task scheduling problems. As scheduling problems are NP-complet...
Avi Rosenfeld, Sarit Kraus, Charlie Ortiz
CMS
2010
207views Communications» more  CMS 2010»
13 years 7 months ago
Statistical Detection of Malicious PE-Executables for Fast Offline Analysis
While conventional malware detection approaches increasingly fail, modern heuristic strategies often perform dynamically, which is not possible in many applications due to related ...
Ronny Merkel, Tobias Hoppe, Christian Krätzer...
ICML
2004
IEEE
14 years 8 months ago
Generalized low rank approximations of matrices
The problem of computing low rank approximations of matrices is considered. The novel aspect of our approach is that the low rank approximations are on a collection of matrices. W...
Jieping Ye