Search Results - Subsampling-based active learning method

Multi-category Classification Problem Oriented Subsampling-Based Active Learning Method

Authors: Liu Zhong Wei Shi Feng Yanghe et al.

Source: Journal of Physics: Conference Series. 1631:012003

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: http://ui.adsabs.harvard.edu/abs/2020JPhCS1631a2003S/abstract

Subsampling oriented active learning method for multi-category classification problem. (English)

Authors: SHI Wei HUANG Honglan FENG Yanghe et al.

Source: Systems Engineering & Electronics; Mar2021, Vol. 43 Issue 3, p700-708, 9p

Subject Terms: PROBLEM solving, CLASSIFICATION

Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning.

Authors: Tian, Xuemeng de Bruin, Sytze Simoes, Rolf et al.

Source: PeerJ; Jul2025, p1-56, 56p

Towards a more inductive world for drug repurposing approaches.

Authors: de la Fuente, Jesus Serrano, Guillermo Veleiro, Uxía et al.

Source: Nature Machine Intelligence; Mar2025, Vol. 7 Issue 3, p495-508, 14p

Sparse kernel deep stacking networks.

Authors: Welchowski, Thomas Schmid, Matthias

Source: Computational Statistics; Sep2019, Vol. 34 Issue 3, p993-1014, 22p

Subject Terms: HIGH-dimensional model representation, FOURIER transforms, DEEP learning, SUPERVISED learning, GENETIC algorithms, MATHEMATICAL regularization, SCHOOL dropouts

Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study: Exploring the Efficacy of Statistical and Deep Learning Methods: A. Hazra et al.

Authors: Hazra, Arnab Nag, Pratik Yadav, Rishikesh et al.

Source: Journal of Agricultural, Biological & Environmental Statistics (JABES); Mar2025, Vol. 30 Issue 1, p231-254, 24p

Subject Terms: PRECIPITABLE water, STATISTICAL learning, GAUSSIAN processes, SKEWNESS (Probability theory), MATHEMATICAL statistics

DECIPHER for learning disentangled cellular embeddings in large-scale heterogeneous spatial omics data.

Authors: Xia, Chen-Rui Cao, Zhi-Jie Gao, Ge

Source: Nature Communications; 8/27/2025, Vol. 16 Issue 1, p1-13, 13p

Subject Terms: DEEP learning, SCALABILITY, MULTIOMICS, CELL communication, BIG data

Belt and Braces: When Federated Learning Meets Differential Privacy.

Authors: Ren, Xuebin Yang, Shusen Zhao, Cong et al.

Source: Communications of the ACM; Dec2024, Vol. 67 Issue 12, p66-77, 12p

Subject Terms: FEDERATED learning, DATA privacy, MACHINE learning, DEEP learning, DATA encryption

Decoding cell fate: integrated experimental and computational analysis at the single-cell level Open Access.

Authors: Zhou, Yutong Hou, Shuyang Miao, Xinhao et al.

Source: Bioinformatics; Nov2025, Vol. 41 Issue 11, p1-18, 18p

Subject Terms: DEVELOPMENTAL biology, REGENERATIVE medicine, COMPUTATIONAL biology, PREDICTION models, CELL differentiation, MULTIOMICS, CELL analysis

Asymptotic Analysis of the Bias–Variance Trade-Off in Subsampling Metropolis–Hastings.

Authors: Liu, Shuang

Source: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 21, p3395, 30p

Subject Terms: ASYMPTOTIC analysis, MARKOV chain Monte Carlo, BAYESIAN analysis, MEAN square algorithms, STATISTICAL sampling, MATHEMATICAL statistics

Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimation.

Authors: Dutschmann, Thomas-Martin Kinzel, Lennart ter Laak, Antonius et al.

Source: Journal of Cheminformatics; 4/28/2023, Vol. 15 Issue 1, p1-16, 16p

Subject Terms: DEEP learning, MACHINE learning, REGRESSION analysis, STANDARD deviations, CHEMINFORMATICS, PREDICTION models

Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model.

Authors: Mutawa, A. M.

Source: Big Data & Cognitive Computing; Jan2025, Vol. 9 Issue 1, p13, 29p

Subject Terms: MACHINE learning, DECISION support systems, MEDICAL personnel, INTENSIVE care units, FEATURE selection

Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision.

Authors: Luo, Xiangzhong Liu, Di Kong, Hao et al.

Source: ACM Transactions on Embedded Computing Systems; Jan2025, Vol. 24 Issue 1, p1-100, 100p

Subject Terms: ARTIFICIAL neural networks, LANGUAGE models, MACHINE learning, TRANSFORMER models, IMAGE recognition (Computer vision), DEEP learning

Bootstrap-Based Stabilization of Sparse Solutions in Tensor Models: Theory, Assessment, and Application.

Authors: Gutiérrez-Sánchez, Gresky Vicente-Galindo, María Purificación Galindo-Villardón, Purificación

Source: Algorithms; Oct2025, Vol. 18 Issue 10, p602, 30p

Subject Terms: GENE expression profiling, LATENT structure analysis, ADAPTIVE sampling (Statistics), REPLICATION (Experimental design), MULTIVARIATE analysis

Optimal Subsampling for Functional Quasi-Mode Regression with Big Data.

Authors: Wang, Tao

Source: Journal of Computational & Graphical Statistics; Apr-Jun2025, Vol. 34 Issue 2, p552-566, 15p

Subject Terms: MONTE Carlo method, ASYMPTOTIC normality, ASYMPTOTIC distribution, MAXIMUM likelihood statistics, SMOOTHNESS of functions

Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon.

Authors: Prabhakaran, Sandhya Yapp, Clarence Baker, Gregory J. et al.

Source: Molecular Oncology; Jun2025, Vol. 19 Issue 6, p1565-1581, 17p

Scalable big earth observation data mining algorithms: a review.

Authors: Sisodiya, Neha Dube, Nitant Prakash, Om et al.

Source: Earth Science Informatics; Sep2023, Vol. 16 Issue 3, p1993-2016, 24p

Subject Terms: DEEP learning, DATA mining, MACHINE learning, STATISTICAL learning, ALGORITHMS, BIG data

PSPAN:pyramid spatially weighted pixel attention network for image dehazing.

Authors: Zhang, YuBo Xu, Tongxiang Tian, Kang

Source: Multimedia Tools & Applications; Jan2024, Vol. 83 Issue 4, p11367-11385, 19p

Subject Terms: PYRAMIDS, COMPUTER vision, IMAGE reconstruction, FEATURE extraction, PIXELS

Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients.

Authors: Meredith, Thomas Mohammed, Farhan Pomeroy, Amy et al.

Source: Frontiers in Cardiovascular Medicine; 2025, p1-11, 11p

Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline.

Authors: Mikolajewicz, Nicholas Gacesa, Rafael Aguilera-Uribe, Magali et al.

Source: Communications Biology; 10/28/2022, Vol. 5 Issue 1, p1-14, 14p

Subject Terms: TRANSCRIPTOMES, ANNOTATIONS, GENE expression, K-nearest neighbor classification, CODEPENDENCY, DATA analysis, SUPERVISED learning