2014
JMLR
JMLR 2014
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
Abstract
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble methods based on binary SVM models. Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constituent models. Experimental results show that using ensemble approaches can drastically reduce training complexity while maintaining high predictive accuracy. The EnsembleSVM software package is freely available online at esat.kuleuven.be/stadius/ensemblesvm. [abs] [ pdf ][ bib ] [ code ] © JMLR 2014. (edit, beta)
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Keyword Pioneer
— training complexity
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Hot Topic Early Bird
— binary classification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio