Title :
On-line off-line Ranking Support Vector Machine and analysis
Author :
Gu, Bin ; Wang, Jiandong ; Chen, Haiyan
Author_Institution :
Dept. of Comput. Sci. & Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing
Abstract :
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.
Keywords :
convex programming; learning (artificial intelligence); support vector machines; convex quadratic programming problem; finite algorithm convergence; online offline ranking support vector machine learning; Algorithm design and analysis; Approximation algorithms; Convergence; Function approximation; Machine learning; Management training; Quadratic programming; Statistical learning; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4633975
