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Support Vector Machines

2007; Wiley; Linguagem: Inglês

10.1007/0-387-37452-3_7

1467-8640

Jaime Gòmez Sàenz de Tejada, Juan Seijas Martìnez-Echevarrìa,

Time Series Analysis and Forecasting

Support Vector Machines is the most recent algorithm in the Machine Learning community. After a bit less than a decade of live, it has displayed many advantages with respect to the best old methods: generalization capacity, ease of use, solution uniqueness. It has also shown some disadvantages: maximum data handling and speed in the training phase. However, these disadvantages will be overcome in the near future, as computer power increases, leaving an all-purpose learning method both cheap to use and giving the best performance. This chapter provides an overview about the main SVM configuration, its mathematical applications and the easiest implementation