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» Learning Functions from Imperfect Positive Data
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COLT
2003
Springer
14 years 23 days ago
Learning from Uncertain Data
The application of statistical methods to natural language processing has been remarkably successful over the past two decades. But, to deal with recent problems arising in this ï¬...
Mehryar Mohri
FSKD
2008
Springer
174views Fuzzy Logic» more  FSKD 2008»
13 years 8 months ago
A Hybrid Re-sampling Method for SVM Learning from Imbalanced Data Sets
Support Vector Machine (SVM) has been widely studied and shown success in many application fields. However, the performance of SVM drops significantly when it is applied to the pr...
Peng Li, Pei-Li Qiao, Yuan-Chao Liu
COLT
2005
Springer
14 years 1 months ago
Separating Models of Learning from Correlated and Uncorrelated Data
We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possibl...
Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andr...
CDC
2009
IEEE
221views Control Systems» more  CDC 2009»
13 years 11 months ago
Parametrization invariant covariance quantification in identification of transfer functions for linear systems
This paper adresses the variance quantification problem for system identification based on the prediction error framework. The role of input and model class selection for the auto-...
Tzvetan Ivanov, Michel Gevers
ICML
2002
IEEE
14 years 8 months ago
Learning the Kernel Matrix with Semi-Definite Programming
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is perfor...
Gert R. G. Lanckriet, Nello Cristianini, Peter L. ...