Generalized robust loss functions for machine learning
Loss function is a critical component of machine learning. Some robust loss functions are
proposed to mitigate the adverse effects caused by noise. However, they still face many …
proposed to mitigate the adverse effects caused by noise. However, they still face many …
EEG-based emotion recognition using random Convolutional Neural Networks
Emotion recognition based on electroencephalogram (EEG) signals is helpful in various
fields, including medical healthcare. One possible medical application is to diagnose …
fields, including medical healthcare. One possible medical application is to diagnose …
[HTML][HTML] Online learning using deep random vector functional link network
Deep neural networks have shown their promise in recent years with their state-of-the-art
results. Yet, backpropagation-based methods may suffer from time-consuming training …
results. Yet, backpropagation-based methods may suffer from time-consuming training …
[HTML][HTML] Structured sparse regularization based random vector functional link networks for DNA N4-methylcytosine sites prediction
As an epigenetic modification that plays an important role in modifying gene function and
controlling gene expression during cell development, DNA N4-methylcytosine (4mC) is still …
controlling gene expression during cell development, DNA N4-methylcytosine (4mC) is still …
Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning–based gamma test variable selection and empirical wavelet transform
This study aims to evaluate the usefulness and effectiveness of four machine learning (ML)
models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the …
models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the …
[HTML][HTML] Prediction of rock blasting induced air overpressure using a self-adaptive weighted kernel ridge regression
Blasting operations are widely recognized as the most frequently used rock breakage
approach in the field of Civil and Mining Engineering. However, the induced air …
approach in the field of Civil and Mining Engineering. However, the induced air …
Jointly optimized ensemble deep random vector functional link network for semi-supervised classification
Randomized neural networks have become more and more attractive recently since they
use closed-form solutions for parameter training instead of gradient-based approaches …
use closed-form solutions for parameter training instead of gradient-based approaches …
1-Norm twin random vector functional link networks based on Universum data for leaf disease detection
Due to rapid climate change and man-made activities, the types of leaf diseases are
gradually increasing. As a result, taking the essential measures to recognize and diagnose …
gradually increasing. As a result, taking the essential measures to recognize and diagnose …
An efficient angle-based twin random vector functional link classifier
Random vector functional link (RVFL) has always proven to be an excellent classifier in
various application areas of machine learning. In this work, inspired by RVFL and its twin …
various application areas of machine learning. In this work, inspired by RVFL and its twin …
Symmetric LINEX loss twin support vector machine for robust classification and its fast iterative algorithm
Q Si, Z Yang, J Ye - Neural Networks, 2023 - Elsevier
Twin support vector machine (TSVM) is a practical machine learning algorithm, whereas
traditional TSVM can be limited for data with outliers or noises. To address this problem, we …
traditional TSVM can be limited for data with outliers or noises. To address this problem, we …