A review on extreme learning machine
Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward
neural network (SLFN), which converges much faster than traditional methods and yields …
neural network (SLFN), which converges much faster than traditional methods and yields …
Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions
The explosive growth of data in volume, velocity and diversity that are produced by medical
applications has contributed to abundance of big data. Current solutions for efficient data …
applications has contributed to abundance of big data. Current solutions for efficient data …
Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China
T Xiong, C Li, Y Bao - Neurocomputing, 2018 - Elsevier
In view of the importance of seasonal forecasting of agricultural commodity price, particularly
vegetable prices, and the limited research attention paid to it previously, this study proposes …
vegetable prices, and the limited research attention paid to it previously, this study proposes …
Functional brain network classification for Alzheimer's disease detection with deep features and extreme learning machine
The human brain can be inherently modeled as a brain network, where nodes denote
billions of neurons and edges denote massive connections between neurons. Analysis on …
billions of neurons and edges denote massive connections between neurons. Analysis on …
Scikit-ELM: an extreme learning machine toolbox for dynamic and scalable learning
This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM
(https://github. com/akusok/scikit-elm, https://scikit-elm. readthedocs. io). Usability and …
(https://github. com/akusok/scikit-elm, https://scikit-elm. readthedocs. io). Usability and …
An image classification framework exploring the capabilities of extreme learning machines and artificial bee colony
AVN Reddy, CP Krishna, PK Mallick - Neural computing and applications, 2020 - Springer
A hybridized image classification strategy is proposed based on discrete wavelet transform,
artificial bee colony (ABC) and extreme learning machine (ELM). The proposed …
artificial bee colony (ABC) and extreme learning machine (ELM). The proposed …
GNEA: a graph neural network with ELM aggregator for brain network classification
Brain networks provide essential insights into the diagnosis of functional brain disorders,
such as Alzheimer's disease (AD). Many machine learning methods have been applied to …
such as Alzheimer's disease (AD). Many machine learning methods have been applied to …
Survey on extreme learning machines for outlier detection
In a two-class classification task, if the number of examples of one class (majority) is much
greater than that of another class (minority), then the classification is said to be class …
greater than that of another class (minority), then the classification is said to be class …
A Five‐Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images
V Srivastava, RK Purwar - Applied Computational Intelligence …, 2017 - Wiley Online Library
This paper presents a two‐dimensional wavelet based decomposition algorithm for
classification of biomedical images. The two‐dimensional wavelet decomposition is done up …
classification of biomedical images. The two‐dimensional wavelet decomposition is done up …
Deep residual learning with dilated causal convolution extreme learning machine
A Sasou - IEEE Access, 2021 - ieeexplore.ieee.org
A feedforward neural network with random weights (RW-FFNN) uses a randomized feature
map layer. This randomization enables the optimization problem to be replaced by a …
map layer. This randomization enables the optimization problem to be replaced by a …