Mika et al.  introduce a non-linear formulation of the Fisher discriminant based the well-known "kernel trick", later shown to be equivalent to the Least-Squares Support Vector Machine [2, 3]. In this paper, we show that the cross-validation error can be computed very efficiently for this class of kernel machine, specifically that leave-one-out cross-validation can be performed with a computational complexity of only O(l3) operations (the same as that of the basic training algorithm), rather than the O(l4) of a direct implementation. This makes leave-one-out cross-validation a practical proposition for model selection in much larger scale applications of KFD classifiers.
|Number of pages||6|
|Publication status||Published - Apr 2003|
|Event||Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003) - Bruges, Belgium|
Duration: 23 Apr 2003 → 25 Apr 2003
|Conference||Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003)|
|Period||23/04/03 → 25/04/03|