[HTML][HTML] Random vector functional link network: recent developments, applications, and future directions
Neural networks have been successfully employed in various domains such as
classification, regression and clustering, etc. Generally, the back propagation (BP) based …
classification, regression and clustering, etc. Generally, the back propagation (BP) based …
An unsupervised parameter learning model for RVFL neural network
With the direct input–output connections, a random vector functional link (RVFL) network is a
simple and effective learning algorithm for single-hidden layer feedforward neural networks …
simple and effective learning algorithm for single-hidden layer feedforward neural networks …
Ensemble deep random vector functional link network using privileged information for Alzheimer's disease diagnosis
In this paper, deep RVFL and its ensembles are enabled to incorporate privileged
information, however, the standard RVFL model and its deep models are unable to use …
information, however, the standard RVFL model and its deep models are unable to use …
Leveraging coupled interaction for multimodal Alzheimer's disease diagnosis
As the population becomes older worldwide, accurate computer-aided diagnosis for
Alzheimer's disease (AD) in the early stage has been regarded as a crucial step for …
Alzheimer's disease (AD) in the early stage has been regarded as a crucial step for …
A study on the relationship between the rank of input data and the performance of random weight neural network
Random feature map** (RFM) is the core operation in the random weight neural network
(RWNN). Its quality has a significant impact on the performance of a RWNN model …
(RWNN). Its quality has a significant impact on the performance of a RWNN model …
An initial study on the relationship between meta features of dataset and the initialization of NNRW
The initialization of neural networks with random weights (NNRW) has a significant impact
on model performance. However, there is no suitable way to solve this problem so far. In this …
on model performance. However, there is no suitable way to solve this problem so far. In this …
An improved fuzziness based random vector functional link network for liver disease detection
There are three challenges in real-life disease detection scenarios: 1) the number of open
samples is small; 2) the difficulty and cost of labeling the samples are very high; 3) The class …
samples is small; 2) the difficulty and cost of labeling the samples are very high; 3) The class …
An elitist artificial electric field algorithm based random vector functional link network for cryptocurrency prices forecasting
Cryptocurrencies have carved out a significant presence in financial transactions during the
past few years. Cryptocurrency market performs similarly to other financial markets with …
past few years. Cryptocurrency market performs similarly to other financial markets with …
Learning cognitive-test-based interpretable rules for prediction and early diagnosis of dementia using neural networks
Background: Accurate, cheap, and easy to promote methods for dementia prediction and
early diagnosis are urgently needed in low-and middle-income countries. Integrating various …
early diagnosis are urgently needed in low-and middle-income countries. Integrating various …
Image classification using convolutional neural networks and kernel extreme learning machines
We know that convolutional neural networks are good at learning invariant features, but not
always optimal for classification. Contrarily, Kernel Extreme Learning Machines (KELMs) are …
always optimal for classification. Contrarily, Kernel Extreme Learning Machines (KELMs) are …