[HTML][HTML] Review of image classification algorithms based on convolutional neural networks
L Chen, S Li, Q Bai, J Yang, S Jiang, Y Miao - Remote Sensing, 2021 - mdpi.com
Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …
emergence of deep learning has promoted the development of this field. Convolutional …
Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne)
Abstract Feature Extraction Algorithms (FEAs) aim to address the curse of dimensionality
that makes machine learning algorithms incompetent. Our study conceptually and …
that makes machine learning algorithms incompetent. Our study conceptually and …
Reproducible brain-wide association studies require thousands of individuals
Magnetic resonance imaging (MRI) has transformed our understanding of the human brain
through well-replicated mapping of abilities to specific structures (for example, lesion …
through well-replicated mapping of abilities to specific structures (for example, lesion …
The path to proton structure at 1% accuracy: NNPDF Collaboration
We present a new set of parton distribution functions (PDFs) based on a fully global dataset
and machine learning techniques: NNPDF4. 0. We expand the NNPDF3. 1 determination …
and machine learning techniques: NNPDF4. 0. We expand the NNPDF3. 1 determination …
A survey of convolutional neural networks: analysis, applications, and prospects
A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …
learning field. Since CNN made impressive achievements in many areas, including but not …
Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks
ID Apostolopoulos, TA Mpesiana - Physical and engineering sciences in …, 2020 - Springer
In this study, a dataset of X-ray images from patients with common bacterial pneumonia,
confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection …
confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection …
A systematic review on overfitting control in shallow and deep neural networks
Shallow neural networks process the features directly, while deep networks extract features
automatically along with the training. Both models suffer from overfitting or poor …
automatically along with the training. Both models suffer from overfitting or poor …
Practitioner's guide to latent class analysis: methodological considerations and common pitfalls
P Sinha, CS Calfee, KL Delucchi - Critical care medicine, 2021 - journals.lww.com
Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and
statistical inference. There has been a recent upsurge in the application of latent class …
statistical inference. There has been a recent upsurge in the application of latent class …
Supervised machine learning: a brief primer
Abstract Machine learning is increasingly used in mental health research and has the
potential to advance our understanding of how to characterize, predict, and treat mental …
potential to advance our understanding of how to characterize, predict, and treat mental …
Machine learning testing: Survey, landscapes and horizons
This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …