Abstract
Since the introduction of support vector machines, numerous variants have been developed. In this chapter, we discuss some of them: least-squares support vector machines, linear programming support vector machines, sparse support vector machines, etc. We also discuss learning paradigms: incremental training, learning using privileged information, semi-supervised learning, multiple classifier systems, multiple kernel learning, and other topics: confidence level and visualization of support vector machines.
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Notes
- 1.
For regular SVMs, this fact was shown in [11].
- 2.
Strictly speaking this statement is wrong, because support vector machines are not linear-transformation invariant. But by cross-validation, the difference of the generalization abilities may be small.
- 3.
Discussions with Prof. H. Motoda.
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Abe, S. (2010). Variants of Support Vector Machines. In: Support Vector Machines for Pattern Classification. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84996-098-4_4
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