On-line off-line Ranking Support Vector Machine and analysis
Bin Gu, Jiandong Wang, Haiyan Chen · 2008
Ranking support vector machine (RSVM) learning is equivalent to solving a convex quadratic programming problem. Currently there exists some difficulties for exact online ranking learning. This paper presents an exact and effective method that can solve the online ranking learning problem and shows the feasibility and finite convergence of the algorithm from the perspective of theoretical analysis. Additionally, this paper extends this method for online learning to offline ranking learning and offers another algorithm for solving large-scale RSVM.