GPU-accelerated and parallelized ELM ensembles for large-scale regression

Heeswijk, Mark van; Miché, Yoan; Oja, Erkki; Lendasse, Amaury

doi:10.1016/j.neucom.2010.11.034

Cited by 151 publications

(45 citation statements)

References 14 publications

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“…Meanwhile, when a more complex model and more variables are introduced, the time for the training process such as feature selection might increase significantly. Thus the parallelization of those algorithms, such as the ELM-based feature selection, might be a promising topic for future implementation [59]. Multi-step head predictions could be studied in the future, which would be able to provide timely forecasts for the public.…”

Section: Future Work and Discussionmentioning

confidence: 99%

Air quality forecasting using neural networks

Zhao

Heeswijk

Karhunen

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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In this thesis project, a special type of neural network: Extreme Learning Machine (ELM) is implemented to predict the air quality based on the air quality time series itself and the external meteorological records. A regularized version of ELM with linear components is chosen to be the main model for prediction. To take full advantage of this model, its hyper-parameters are studied and optimized. Then a set of variables is selected (or constructed) to maximize the performance of ELM, where two different variable selection methods (i.e. wrapper and filtering methods) are evaluated. The wrapper method ELM-based forward selection is chosen for the variable selection. Meanwhile, a feature extraction method (Principal Component Analysis) is implemented in the hope of reducing the candidate meteorological variables for feature selection, which proves to be helpful. At last, with all the parameters being properly optimized, ELM is used for the prediction and generates satisfying results.

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Section: Future Work and Discussionmentioning

confidence: 99%

Air quality forecasting using neural networks

Zhao

Heeswijk

Karhunen

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…The multilayer learning architecture that uses ELM auto-encoder [11] and subnetwork nodes [12] expands ELM from a single layer structure to a multilayer structure. Also, recent applications of ELM have included: machine vision [13,14], ensemble learning [15,16], sparse learning [17,18] big data applications [19,20], etc.…”

Section: Smooth Average Congestedmentioning

confidence: 99%

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

2017

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Abstract:There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition.

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“…(6), it can be seen that a large part of the HAT-matrix consists of H † , the Moore-Penrose generalized inverse of the matrix H. Therefore, by explicitly computing H † , and reusing H † to compute the LOO error MSE PRESS , model structure selection of the ELM comes at very low overhead. A detailed description of this approach can be found in [17]. In summary, the algorithm for training and LOO-based model structure selection of ELM is stated in Algorithm 2.…”

Section: Efficient Loo Computation and Model Selectionmentioning

confidence: 99%

Binary/ternary extreme learning machines

Heeswijk

Miché

2015

Neurocomputing

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a b s t r a c tIn this paper, a new hidden layer construction method for Extreme Learning Machines (ELMs) is investigated, aimed at generating a diverse set of weights. The paper proposes two new ELM variants: Binary ELM, with a weight initialization scheme based on f0; 1g-weights; and Ternary ELM, with a weight initialization scheme based on fÀ1; 0; 1g-weights. The motivation behind this approach is that these features will be from very different subspaces and therefore each neuron extracts more diverse information from the inputs than neurons with completely random features traditionally used in ELM. Therefore, ideally it should lead to better ELMs. Experiments show that indeed ELMs with ternary weights generally achieve lower test error. Furthermore, the experiments show that the Binary and Ternary ELMs are more robust to irrelevant and noisy variables and are in fact performing implicit variable selection. Finally, since only the weight generation scheme is adapted, the computational time of the ELM is unaffected, and the improved accuracy, added robustness and the implicit variable selection of Binary ELM and Ternary ELM come for free.

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GPU-accelerated and parallelized ELM ensembles for large-scale regression

Cited by 151 publications

References 14 publications

Air quality forecasting using neural networks

Air quality forecasting using neural networks

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

Binary/ternary extreme learning machines

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