Abstract
Support Vector Machines (SVMs) have been recently introduced as a new method for function estimation. In this survey we first review the main theoretical properties of SVMs, then present an implementation of SVMs able to work with training sets of very large size. Finally, we discuss two computer vision applications in which SVMs for both pattern recognition and regression estimation have been successfully employed.
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© 1999 Springer-Verlag London Limited
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Pittore, M., Verri, A. (1999). Theory, Implementation, and Applications of Support Vector Machines. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN Vietri-99. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0877-1_4
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DOI: https://doi.org/10.1007/978-1-4471-0877-1_4
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1226-6
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