Inductive bias for semi-supervised extreme learning machine

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• 作者：Federica Bisio^a ; ^{federica.bisio@edu.unige.it" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Sergio Decherchi^b ; ^{sergio.decherchi@iit.it" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Paolo Gastaldo^a ; ^{paolo.gastaldo@unige.it" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Rodolfo Zunino^a ; ^{rodolfo.zunino@unige.it" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Extreme Learning Machine ; Semi-supervised learning ; Inductive bias
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：22 January 2016
• 年：2016
• 卷：174
• 期：part_PA
• 页码：154-167
• 全文大小：2756 K

文摘

This research shows that inductive bias provides a valuable method to effectively tackle semi-supervised classification problems. In the learning theory framework, inductive bias provides a powerful tool, and allows one to shape the generalization properties of a learning machine. The paper formalizes semi-supervised learning as a supervised learning problem biased by an unsupervised reference solution. The resulting semi-supervised classification framework can apply any clustering algorithm to derive the reference function, thus ensuring maximum flexibility. In this context, the paper derives the biased version of Extreme Learning Machine (br-ELM). The experimental session involves several real world problems and proves the reliability of the semi-supervised classification scheme.