title: Extreme learning machine for ranking: generalization analysis and applications.
abstract: The extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression. In this paper, we investigate the generalization performance of ELM-based ranking. A new regularized ranking algorithm is proposed based on the combinations of activation functions in ELM. The generalization analysis is established for the ELM-based ranking (ELMRank) in terms of the covering numbers of hypothesis space. Empirical results on the benchmark datasets show the competitive performance of the ELMRank over the state-of-the-art ranking methods.
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, China. Electronic address: chenhongml@163.com.
authors.forename: Hong
authors.initials: H
authors.lastname: Chen
authors.affiliation: Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China; Department of Computer and Information Science, University of Macau, Macau 999078, China. Electronic address: pengjt1982@126.com.
authors.forename: Jiangtao
authors.initials: J
authors.lastname: Peng
authors.affiliation: Department of Computer and Information Science, University of Macau, Macau 999078, China. Electronic address: yicongzhou@umac.mo.
authors.forename: Yicong
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China. Electronic address: lilq@hubu.edu.cn.
authors.forename: Luoqing
authors.initials: L
authors.lastname: Li
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, China. Electronic address: zhibinpan2008@gmail.com.
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D021281:Quantitative Structure-Activity Relationship
keywords: Coefficient regularization; Extreme learning machine; Generalization bound; Learning theory; Ranking