109 citations · 209 across the 3 of their papers we have counts for
4 papers
Assessing binary classifiers using only positive and unlabeled data
Marc Claesen, Jesse Davis, Frank De Smet +1
Assessing the performance of a learned model is a crucial part of machine learning. However, in some domains only positive and unlabeled examples are available, which prohibits the…
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
Marc Claesen, Frank De Smet, Johan Suykens +1
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble method…
Fast Prediction with SVM Models Containing RBF Kernels
Marc Claesen, Frank De Smet, Johan A. K. Suykens +1
We present an approximation scheme for support vector machine models that use an RBF kernel. A second-order Maclaurin series approximation is used for exponentials of inner product…
A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models
Marc Claesen, Frank De Smet, Johan A. K. Suykens +1
We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task…