Multicategory Support Vector Machines, Theory, and Application to the Classification of . . .

2003

Citations in the corpus

• CHARACTERIZING THE SOLUTION PATH OF MULTICATEGORY SUPPORT VECTOR MACHINES (2006, background): “A few examples include smoothing splines (Wahba (1990)), penalized logistic regression, and support vector machines (Vapnik (1998)).”
• A comparative study of machine learning classifiers for modeling travel mode choice (2017, background): “This approach generally tends to296 increase the classifier’s variance, because only small subsets of the data are used to learn to distinguish297 between each pair of classes (Lee et al., 2004).”
• Multi-output learning via spectral filtering (2012, methodology): “Finally, we note that the use of vector-valued kernels for multi-category classification has not been analyzed yet, though we will see that it is implicit in methods such as multi-category Support Vector Machines (Lee et al. 2004).”