On extending extreme learning machine to non-redundant synergy pattern based graph classification

Wang, Zhanghui; Zhao, Yuhai; Wang, Guoren; Li, Yuan; Wang, Xue

doi:10.1016/j.neucom.2013.11.057

Cited by 18 publications

(1 citation statement)

References 25 publications

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“…Two commonly-used methods for multi-class classification are one-against-all (OAA) and one-against-one (OAO) [41]. For the N-class problem, OAA builds N binary classifiers, one for each class separating the class from the others.…”

Section: Elm Ensemble Based On Gamentioning

confidence: 99%

Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

Yin

Zhao

et al. 2016

Applied Sciences

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Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks:(1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML)-extreme learning machine (ELM), to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.

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Section: Elm Ensemble Based On Gamentioning

confidence: 99%