Online handwriting recognition with support vector machines - a kernel approach

By Claus Bahlmann, Bernard Haasdonk, Hans Burkhardt. Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, 2002

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A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “SVMs have been prominently used in a number of challenging applications such as handwriting recognition [222], and cancer genomics[223].”
Improving SVM accuracy by training on auxiliary data sources (2004, background): “The practical consequences of this are not clear, and other authors have found that empirical \kernels" of this sort work very well (Bahlmann et al., 2002).”
Learning with Distance Substitution Kernels (2004, methodology): “But also in the non-pd case they can be useful, as non-cpd kernels have shown convincing results in SVMs [1,4,8,11].”
Feature space interpretation of SVMs with indefinite kernels (2005, background): “This has been realized in various publications [8], [9], [10], [11], [12].”
Fast Global Alignment Kernels (2011, methodology): “The DTW distance has been used nonetheless with kernel machines by many authors (Bahlmann et al., 2002; Shimodaira et al., 2002; Zhou et al., 2010, etc.) who correct numerically any deficiency of the Gram matrices produced by DTW distances or more simply consider their square (Gudmundsson et al.).”