Regularization path for Ranking SVM.
Karina Zapién Arreola, Thomas Gärtner, Gilles Gasso, Stéphane Canu · The European Symposium on Artificial Neural Networks · 2008
Ranking algorithms are often introduced with the aim of auto- matically personalising search results. However, most ranking algorithms developed in the machine learning community rely on a careful choice of some regularisation parameter. Building upon work on the regularisation path for kernel methods, we propose a parameter selection algorithm for ranking SVM. Empirical results are promising.