A novel extreme learning machine using privileged information

Abstract

Extreme learning machine (ELM) is a competitive machine learning technique, which is much more efficient and usually lead to better generalization performance compared to the traditional classifiers. In order to further improve its performance, we proposed a novel ELM called ELM+ which introduces the privileged information to the traditional ELM method. This privileged information, which is ignored by the classical ELM but often exists in human teaching and learning, will optimize the training stage by constructing a set of correcting functions. We demonstrate the performance of ELM+ on datasets from UCI machine learning repository, Mackey–Glass time series and radar emitter recognition and also present the comparison with SVM, ELM and SVM+. The experimental results indicate the validity and advantage of our method.