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Linear1 Support Vector Machines (e.g., SVMperf, Pegasos,
LIBLINEAR) are powerful and extremely efficient classification
tools when the datasets are very large and/or highdimensional,
which is commo...
A bagging SVM to learn from positive and unlabeled examples
A bagging SVM learn from positive unlabeled examples
2010/10/14
We consider the problem of learning a binary classifier from a training set of positive and unlabeled examples, both in the inductive and in the transductive setting. This problem, often referred to a...
Variable selection for multicategory SVM via adaptive sup-norm regularization
Classification L1-norm penalty multicategory sup-norm SVM
2009/9/16
Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatica...
Un résultat de consistance pour des SVM fonctionnels par interpolation spline
Un résultat consistance pour SVM fonctionnels interpolation spline
2010/4/29
This note proposes a new methodology for function classification with Support Vector Machine (SVM). Rather than relying on projection on a truncated Hilbert basis as in our previous work, we use an im...