A comprehensive survey of machine learning methodologies with emphasis in water resources management
This paper offers a comprehensive overview of machine learning (ML) methodologies and
algorithms, highlighting their practical applications in the critical domain of water resource …
algorithms, highlighting their practical applications in the critical domain of water resource …
Hardware trojan detection using machine learning: A tutorial
With the growth and globalization of IC design and development, there is an increase in the
number of Designers and Design houses. As setting up a fabrication facility may easily cost …
number of Designers and Design houses. As setting up a fabrication facility may easily cost …
Diversified regularization enhanced training for effective manipulator calibration
Recently, robot arms have become an irreplaceable production tool, which play an important
role in the industrial production. It is necessary to ensure the absolute positioning accuracy …
role in the industrial production. It is necessary to ensure the absolute positioning accuracy …
Inter-class sparsity based discriminative least square regression
Least square regression is a very popular supervised classification method. However, two
main issues greatly limit its performance. The first one is that it only focuses on fitting the …
main issues greatly limit its performance. The first one is that it only focuses on fitting the …
Advancing non-negative latent factorization of tensors with diversified regularization schemes
Dynamic relationships are frequently encountered in big data and services computing-
related applications, like dynamic data of user-side QoS in Web services. They are modeled …
related applications, like dynamic data of user-side QoS in Web services. They are modeled …
Low-rank preserving projection via graph regularized reconstruction
Preserving global and local structures during projection learning is very important for feature
extraction. Although various methods have been proposed for this goal, they commonly …
extraction. Although various methods have been proposed for this goal, they commonly …
TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model
K Yan, H Lv, Y Guo, Y Chen, H Wu, B Liu - Bioinformatics, 2022 - academic.oup.com
Motivation Therapeutic peptide prediction is important for the discovery of efficient
therapeutic peptides and drug development. Researchers have developed several …
therapeutic peptides and drug development. Researchers have developed several …
Tensorized incomplete multi-view clustering with intrinsic graph completion
Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a
consensus representation from different views but ignore the important information hidden in …
consensus representation from different views but ignore the important information hidden in …
Self-taught multi-view spectral clustering
By integrating multiple views, ie, multi-view learning (ML), we can discover the underlying
data structures so that the performance of learning tasks can improve. As a basic and …
data structures so that the performance of learning tasks can improve. As a basic and …
Feature concatenation multi-view subspace clustering
Multi-view clustering is a learning paradigm based on multi-view data. Since statistic
properties of different views are diverse, even incompatible, few approaches implement …
properties of different views are diverse, even incompatible, few approaches implement …