Extreme Learning Machine for Multilayer Perceptron

By Jiexiong Tang, Chenwei Deng, G. Huang. IEEE Transactions on Neural Networks and Learning Systems, 2016

1,341 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2878d9936f494ed7d0c8aec47e9bcc5e51609f9a

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Citations in the corpus

Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction (2019, methodology): “and industry [58]–[61], DNM is established to become an important method as an alternative in these fields; and 4)”
A review of neural networks in plant disease detection using hyperspectral data (2018, background): “proposed to train single hidden-layer forward network [101,102], and this concept has been extended to multihidden-layer networks [103] and kernel learning [104].”
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, background): “Multilayered-deep extreme learning (DELM) [111] predicts the ship’s speed, based on the features collected from on-board sensors, such as designed and ground speed, draft, engine and shaft generator power, wind speed, temperature, fuel consumption, etc.”
Review of Deep Learning Algorithms and Architectures (2019, background): “came up with a multilayer version of ELM in 2016 [70] to take on more complex problems.”
NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search (2022, methodology): “Besides the above search strategies, the extreme learning machine (ELM) [37], [38] and broad learning system (BLS) [39] can also be used to design the structure of neural networks automatically.”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, methodology): “In contrast to deep network paradigm, an extreme learning machine (ELM) based hierarchical learning framework (H-ELM) proposed in [219] claimed a faster learning than deep learning by ELM [220] based auto-encoding.”
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture (2018, methodology): “To prove the effectiveness of BLS, we will compare the classification ability of our method with existing mainstream methods, including stacked auto encoders (SAE) [6], another version of stacked autoencoder (SDA) [52], DBN [5], multilayer perceptron-based methods (MLP) [53], DBM [7], two kinds of e”