Extreme learning machine for ranking: Generalization analysis and applications

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• 作者：Hong Chen^a ; ^{chenhongml@163.com" class="auth_mail} ; ^{chenh@mail.hzau.edu.cn" class="auth_mail} ; Jiangtao Peng^b ; ^c ; ^{pengjt1982@126.com" class="auth_mail} ; Yicong Zhou^c ; ^{yicongzhou@umac.mo" class="auth_mail} ; Luoqing Li^b ; ^{lilq@hubu.edu.cn" class="auth_mail} ; Zhibin Pan^a ; ^{zhibinpan2008@gmail.com" class="auth_mail}
• 关键词：Learning theory ; Ranking ; Extreme learning machine ; Coefficient regularization ; Generalization bound
• 刊名：Neural Networks
• 出版年：May, 2014
• 年：2014
• 卷：53
• 期：Complete
• 页码：119-126
• 全文大小：481 K

文摘

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.