Cholesky Factorization Based Online Sequential Extreme Learning Machines with Persistent Regularization and Forgetting Factor

The online sequential extreme learning machine with persistent regularization and forgetting factor (OSELM-PRFF) can avoid potential singularities or ill-posed problems of online sequential regularized extreme learning machines with forgetting factors (FR-OSELM), and is particularly suitable for modelling in non-stationary environments. However, existing algorithms for OSELM-PRFF are time-consuming or unstable in certain paradigms or parameters setups. This paper presents a novel algorithm for OSELM-PRFF, named “Cholesky factorization based” OSELM-PRFF (CF-OSELM-PRFF), which recurrently constructs an equation for extreme learning machine and efficiently solves the equation via Cholesky factorization during every cycle. CF-OSELM-PRFF deals with timeliness of samples by forgetting factor, and the regularization term in its cost function works persistently. CF-OSELM-PRFF can learn data one-by-one or chunk-by-chunk with a fixed or varying chunk size. Detailed performance comparisons between CF-OSELM-PRFF and relevant approaches are carried out on several regression problems. The numerical simulation results show that CF-OSELM-PRFF demonstrates higher computational efficiency than its counterparts, and can yield stable predictions.

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Comparison of Several Extreme Learning Machine Algorithm for Modeling Concrete Compressive Strength

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.1735 ◽

2014 ◽

Vol 548-549 ◽

pp. 1735-1738 ◽

Cited By ~ 1

Author(s):

Jian Tang ◽

Dong Yan ◽

Li Jie Zhao

Keyword(s):

Compressive Strength ◽

Extreme Learning Machine ◽

Prediction Accuracy ◽

Partial Least Square ◽

Least Square ◽

Extreme Learning Machines ◽

Concrete Compressive Strength ◽

Learning Machines ◽

Learning Machine

Modeling concrete compressive strength is useful to ensure quality of civil engineering. This paper aims to compare several Extreme learning machines (ELMs) based modeling approaches for predicting the concrete compressive strength. Normal ELM algorithm, Partial least square-based extreme learning machines (PLS-ELMs) algorithm and Kernel ELM (KELM) algorithm are used and evaluated. Results indicate that the normal ELMs algorithm has the highest modeling speed, and the KELM has the best prediction accuracy. Every method is validated for modeling concrete compressive strength. The appropriate modeling approach should be selected according different purposes.

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Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism

Mathematical Problems in Engineering ◽

10.1155/2014/938548 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Xinran Zhou ◽

Zijian Liu ◽

Congxu Zhu

Keyword(s):

Extreme Learning Machine ◽

Feedforward Neural Networks ◽

Matrix Inversion ◽

Time Varying ◽

Extreme Learning Machines ◽

Learning Machines ◽

Matrix Expansion ◽

Learning Machine ◽

Hidden Layer ◽

Forgetting Mechanism

To apply the single hidden-layer feedforward neural networks (SLFN) to identify time-varying system, online regularized extreme learning machine (ELM) with forgetting mechanism (FORELM) and online kernelized ELM with forgetting mechanism (FOKELM) are presented in this paper. The FORELM updates the output weights of SLFN recursively by using Sherman-Morrison formula, and it combines advantages of online sequential ELM with forgetting mechanism (FOS-ELM) and regularized online sequential ELM (ReOS-ELM); that is, it can capture the latest properties of identified system by studying a certain number of the newest samples and also can avoid issue of ill-conditioned matrix inversion by regularization. The FOKELM tackles the problem of matrix expansion of kernel based incremental ELM (KB-IELM) by deleting the oldest sample according to the block matrix inverse formula when samples occur continually. The experimental results show that the proposed FORELM and FOKELM have better stability than FOS-ELM and have higher accuracy than ReOS-ELM in nonstationary environments; moreover, FORELM and FOKELM have time efficiencies superiority over dynamic regression extreme learning machine (DR-ELM) under certain conditions.

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Spatial extreme learning machines: An application on prediction of disease counts

Statistical Methods in Medical Research ◽

10.1177/0962280218767985 ◽

2018 ◽

Vol 28 (9) ◽

pp. 2583-2594

Author(s):

Marcos O Prates

Keyword(s):

Learning Community ◽

Extreme Learning Machine ◽

Spatial Modeling ◽

Real Data ◽

Extreme Learning Machines ◽

Missing Information ◽

Learning Machines ◽

Uncertainty Prediction ◽

Learning Machine ◽

Many Sources

Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

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Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

MATEC Web of Conferences ◽

10.1051/matecconf/202030904018 ◽

2020 ◽

Vol 309 ◽

pp. 04018

Author(s):

Guangjie Hao ◽

Menghong Yu ◽

Zhen Su

Keyword(s):

Extreme Learning Machine ◽

Prediction Models ◽

Prediction Method ◽

Bat Algorithm ◽

Yangtze Estuary ◽

Extreme Learning Machines ◽

Learning Machines ◽

Density Prediction ◽

Learning Machine ◽

Selection Of

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.

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Klasifikasi Keadaan Mata Berdasarkan sinyal EEG menggunakan Extreme Learning Machines

Indonesian Journal on Computing (Indo-JC) ◽

10.21108/indojc.2016.1.2.105 ◽

2016 ◽

Vol 1 (2) ◽

pp. 97 ◽

Cited By ~ 1

Author(s):

Ersa Christian Prakoso ◽

Untari Novia Wisesty ◽

Jondri .

Keyword(s):

Extreme Learning Machine ◽

Extreme Learning Machines ◽

White Space ◽

Font Size ◽

Stroke Width ◽

Learning Machines ◽

Normal Line ◽

Learning Machine

Electroencephalography atau sinyal EEG adalah salah satu biosignal yang marak menjadi topik penelitian saat ini. Sinyal EEG memiliki banyak manfaat seperti pendeteksian epilepsi, gangguan tidur, atau input dalam aplikasi komputer. Salah satu input yang dapat dideteksi berdasarkan sinyal EEG adalah keadaan mata. Namun untuk digunakan sebagai input dalam aplikasi diperlukan klasifikasi dengan performansi yang memadai. Oleh karena itu penulis akan dilakukan penelitian dimana salah satu metode pembelajaran Jaringan Syaraf Tiruan yaitu Extreme Learning Machine (ELM) akan diimplementasikan untuk mengklasifikasikan kondisi mata berdasarkan sinyal EEG. Dataset yang digunakan untuk melatih dan menguji model adalah dataset eye-state yang didonasikan oleh Oliver Roesler digabung dengan dataset yang berasal dari website repository Universitas of California, IrvineI (UCI) . Terdapat 7 corpus yang terdiri dari perekaman EEG yang dilakukan kepada 4 orang berbeda, lalu ditambahkan 1 corpus yang merupakan penggabungan seluruh corpus lain. Dari hasil pengujian yang dilakukan disimpulkan bahwa ELM dapat digunakan untuk klasifikasi keadaan mata dengan akurasi mencapai 97,95% dengan waktu latih hanya 0,81 detik jika masing-masing data digunakan secara terpisah, sedangkan penggabungan keseluruhan dataset hanya mencapai akurasi 78,94% dengan waktu latih 5,71 detik.<br style="font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px;" />

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Evolutionary Voting-Based Extreme Learning Machines

Mathematical Problems in Engineering ◽

10.1155/2014/808292 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Nan Liu ◽

Jiuwen Cao ◽

Zhiping Lin ◽

Pin Pin Pek ◽

Zhi Xiong Koh ◽

...

Keyword(s):

Genetic Algorithm ◽

Decision Making ◽

Extreme Learning Machine ◽

Real World ◽

Classification Accuracy ◽

Majority Voting ◽

Extreme Learning Machines ◽

Learning Machines ◽

Learning Machine ◽

Elm Classifier

Voting-based extreme learning machine (V-ELM) was proposed to improve learning efficiency where majority voting was employed. V-ELM assumes that all individual classifiers contribute equally to the decision ensemble. However, in many real-world scenarios, this assumption does not work well. In this paper, we aim to enhance V-ELM by introducing weights to distinguish the importance of each individual ELM classifier in decision making. Genetic algorithm is used for optimizing these weights. This evolutionary V-ELM is named as EV-ELM. Results on several benchmark databases show that EV-ELM achieves the highest classification accuracy compared with V-ELM and ELM.

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Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

Sustainability ◽

10.3390/su13073744 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3744

Author(s):

Mingcheng Zhu ◽

Shouqian Li ◽

Xianglong Wei ◽

Peng Wang

Keyword(s):

Extreme Learning Machine ◽

Prediction Models ◽

Good Prediction ◽

Foundation Settlement ◽

Machine Method ◽

Settlement Prediction ◽

Soft Foundation ◽

Simulation Results ◽

Learning Machine ◽

The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

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Parsimonious kernel extreme learning machine in primal via Cholesky factorization

Neural Networks ◽

10.1016/j.neunet.2016.04.009 ◽

2016 ◽

Vol 80 ◽

pp. 95-109 ◽

Cited By ~ 14

Author(s):

Yong-Ping Zhao

Keyword(s):

Extreme Learning Machine ◽

Cholesky Factorization ◽

Kernel Extreme Learning Machine ◽

Learning Machine

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Convolutional Extreme Learning Machines: A Systematic Review

10.20944/preprints202104.0753.v1 ◽

2021 ◽

Author(s):

Iago Richard Rodrigues ◽

Sebastião Rogério ◽

Judith Kelner ◽

Djamel Sadok ◽

Patricia Takako Endo

Keyword(s):

Systematic Review ◽

Deep Learning ◽

Extreme Learning Machine ◽

Research Area ◽

New Paradigm ◽

Learning Machines ◽

Starting Point ◽

Benchmark Datasets ◽

Good Starting Point ◽

Learning Machine

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

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A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2015/260970 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 8

Author(s):

Pengbo Zhang ◽

Zhixin Yang

Keyword(s):

Extreme Learning Machine ◽

Learning Algorithm ◽

Approximation Accuracy ◽

Weak Learner ◽

Fast Learning ◽

Learning Speed ◽

Regression Problems ◽

Learning Machine ◽

The Stability ◽

Stability And Accuracy

Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost.RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost.RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner’s performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of “weak” learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.

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