Multisurface proximal support vector machine classification via generalized eigenvalues

By Olvi L. Mangasarian, Edward W. Wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

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Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “There are improved versions of random forest reported in the literature: stratified random forest [68] with weighted feature sampling; Instance based random forest [69] for better tree selection; Extremely randomized trees [70] with randomly generated threshold for randomly selected features and its”
TSVR: An efficient Twin Support Vector Machine for regression (2010, background): “Recently, Jayadeva, Khemchandani, and Chandra (2007) have proposed a Twin Support Vector Machine (TSVM) classifier for binary data classification, which is in the spirit of generalized eigenvalue proximal support vector machine (GEPSVM) (Mangasarian & Wild, 2006).”
Twin Support Vector Machines for Pattern Classification (2007, methodology): “� Testing accuracy figures have been obtained from [ 5 ].”
Improvements on Twin Support Vector Machines (2011, methodology): “First, consider a simple 2-D “Cross Planes” dataset, which was tested in [ 7 ] to indicate that their TWSVM can handle the cross planes dataset much better compared with SVC.”