Extreme learning machine for ranking: Generalization analysis and applications

被引：48

作者：

Chen, Hong ^{[1
]}

Peng, Jiangtao ^{[2
,3
]}

Zhou, Yicong ^{[3
]}

Li, Luoqing ^{[2
]}

Pan, Zhibin ^{[1
]}

机构：

[1] Huazhong Agr Univ, Coll Sci, Wuhan 430070, Peoples R China

[2] Hubei Univ, Fac Math & Stat, Wuhan 430062, Peoples R China

[3] Univ Macau, Dept Comp & Informat Sci, Macau 999078, Peoples R China

来源：

NEURAL NETWORKS | 2014年 / 53卷

基金：

中国国家自然科学基金;

关键词：

Learning theory; Ranking; Extreme learning machine; Coefficient regularization; Generalization bound; ERROR ANALYSIS; REGULARIZATION; PERFORMANCE; ALGORITHM;

D O I：

10.1016/j.neunet.2014.01.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：119 / 126

页数：8

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