Results 111 to 120 of about 7,571,754 (304)
Label-sensitive task grouping by Bayesian nonparametric approach for multi-task multi-label learning
, 2018 © 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Multi-label learning is widely applied in many real-world applications, such as image and gene annotation. While most of the existing multi-label learning models focus
V Nguyen (9860309) +5 more
core
Partial Multi-Label Learning with Label Distribution
, 2020 Partial multi-label learning (PML) aims to learn from training examples each associated with a set of candidate labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate
Xu, Ning, Liu, Yun-Peng, Geng, Xin
core +1 more source
Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows
Advanced Engineering Materials, EarlyView.A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba +5 more
wiley +1 more source
DLKN-MLC: A Disease Prediction Model via Multi-Label Learning. [PDF]
Int J Environ Res Public Health, 2022 Li B, Zhang Y, Wu X.
europepmc +1 more source
Advanced Engineering Materials, EarlyView.Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities. [PDF]
Bioinformatics, 2022 Pang Y, Yao L, Xu J, Wang Z, Lee TY.
europepmc +1 more source
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data. [PDF]
PLoS Comput Biol, 2022 Su R, Yang H, Wei L, Chen S, Zou Q.
europepmc +1 more source
Learning Distance Metrics for Multi-Label Classification [PDF]
, 2016 Distance metric learning is a well studied problem in the field of machine learning, where it is typically used to improve the accuracy of instance based learning techniques.
Pfahringer, Bernhard +2 more
core +1 more source
Scalable and efficient multi-label classification for evolving data streams [PDF]
, 2012 Many challenging real world problems involve multi-label data streams. Efficient methods exist for multi-label classification in non-streaming scenarios.
Pfahringer, Bernhard +3 more
core +1 more source