A fast recognition framework based on extreme learning machine using hybrid object information

Minhas, Rashid; Mohammed, Abdul Adeel; Wu, Qi

doi:10.1016/j.neucom.2009.11.049

Cited by 25 publications

(10 citation statements)

References 16 publications

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“…Therefore, it accelerates the calculation speed greatly. In recent years, ELM has been increasingly popular in classification tasks due to its high generalization ability and fast learning speed (Liang et al, 2006;Minhas et al, 2010;Zhang et al, 2007;Kim et al, 2007). In this study, EEG signals pertaining to two subject groups are investigated; the two groups are: (1) epileptic subjects during a seizure-free interval (interictal EEGs) and (2) epileptic subjects during a seizure (ictal EEGs).…”

Section: Introductionmentioning

confidence: 99%

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Song

Crowcroft

Zhang

2012

Journal of Neuroscience Methods

225

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Section: Introductionmentioning

confidence: 99%

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Song

Crowcroft

Zhang

2012

Journal of Neuroscience Methods

225

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“…Such a learning scheme can operate at extremely faster speed than learning methods of traditional learning frameworks. Improved generalization performance of ELM with the smallest training error and the norm of weights demonstrate its superior classification capability for real-time applications at an exceptionally fast pace without any learning bottleneck [35]. …”

Section: Methodsmentioning

confidence: 99%

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis

et al. 2013

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BackgroundProtein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs.ResultsWe present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance.ConclusionsWhen performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time.

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“…In comparison with the traditional NN, the ELM has a number of advantages such as celerity of training speed, convenience for implementation, and minimal human intervention. The ELM has been frequently used in situations such as human face recognition [40], object classification, hypersonic flight and so on [41]. An online sequential learning algorithm (OS-ELM) for SLFNs with high rapidity and accuracy was developed in [42].…”

Section: Introductionmentioning

confidence: 99%

Haptic Identification by ELM-Controlled Uncertain Manipulator

Yang

Huang

Cheng

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

146

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Abstract-This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.

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A fast recognition framework based on extreme learning machine using hybrid object information

Cited by 25 publications

References 16 publications

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis

Haptic Identification by ELM-Controlled Uncertain Manipulator

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