Approximation Analysis of Gradient Descent Algorithm for Bipartite Ranking
Hong Chen, Fangchao He, Zhibin Pan · Journal of Applied Mathematics · 2012
We introduce a gradient descent algorithm for bipartite ranking with general convex losses. The implementation of this algorithm is simple, and its generalization performance is investigated. Explicit learning rates are presented in terms of the suitable choices of the regularization parameter and the step size. The result fills the theoretical gap in learning rates for ranking problem with general convex losses.