Abstract As a special deep learning algorithm, the multilayer extreme machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from autoencoder (ELM-AE), and its generalization performance affected by representation of ELM-AE. However, given label information, unsupervised ELM-AE difficult build discriminative feature space for classif...

Abstract Our research is devoted to answering whether randomisation-based learning can be fully competitive with the classical feedforward neural networks trained using backpropagation algorithm for classification and regression tasks. We chose extreme as an example of networks. The models were evaluated in reference training time achieved efficiency. conducted extensive comparison these two me...

Single Class Classification (SCC) is the problem to distinguish one class of data (called positive class) from the rest data of multiple classes (negative class). SCC problems are common in real world where positive and unlabeled data are available but negative data is expensive or very hard to acquire. In this paper, extreme leaning machine (ELM), a recently developed machine learning algorith...

This paper presented a novel procedure based on the ensemble empirical mode decomposition and extreme learning machine. Firstly, EEMD was utilized to decompose the vibration signals into a number of IMFs adaptively and the permutation entropy of each IMF was calculated to generate the fault feature matrix. Secondly, a new extreme learning machine was proposed by combining ensemble extreme learn...