[table width =»100%» style =»» responsive =»false»]
[table_head]
[th_column]Título:[/th_column]
[th_column]Rosen’s Projection Method for SVM Training. ESANN 2009, European Symposium on Artificial Neural Networks. Proceedings. ESAN 183-188. 22-24 April 22-24, 2009. Bruges, Belgium.[/th_column]
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[table_body]
[table_row]
[row_column]Autor:[/row_column]
[row_column]López, J., & Dorronsoro, J.R[/row_column]
[/table_row]
[table_row]
[row_column]Año:[/row_column]
[row_column]2009[/row_column]
[/table_row]
[table_row]
[row_column]Enlace:[/row_column]
[row_column]http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.227.5376&rep=rep1&type=pdf[/row_column]
[/table_row]
[/table]
[panel style=»panel-info»]
[panel-header]
Abstract
[/panel-header]
[panel-content]In this work we will give explicit formulae for the application of Rosen’s gradient projection method to SVM training that leads to a very simple implementation. We shall experimentally show that the method provides good descent directions that result in less training iterations, particularly when large precision is wanted. However, a naive kernelization may end up in a procedure requiring more KOs than SMO and further work is needed to arrive at an efficient implementation.[/panel-content]
[/panel]