Results 31 to 40 of about 2,291,078 (312)
How Much a Model be Trained by Passive Learning Before Active Learning?
IEEE Access, 2022 Most pool-based active learning studies have focused on query strategy for active learning. In this paper, via empirical analysis on the effect of passive learning before starting active learning, we reveal that the amount of data acquired by passive ...
Dae Ung Jo, Sangdoo Yun, Jin Young Choi
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Machine Learning Prediction of the Redox Activity of Quinones
Materials, 2023 The redox properties of quinones underlie their unique characteristics as organic battery components that outperform the conventional inorganic ones. Furthermore, these redox properties could be precisely tuned by using different substituent groups. Machine learning and statistics, on the other hand, have proven to be very powerful approaches for the ...
Ilia Kichev +3 more
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Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
Applied Sciences, 2022 Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as ...
Mingfei Wu, Chen Li, Zehuan Yao
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Supervised machine learning and active learning in classification of radiology reports [PDF]
Journal of the American Medical Informatics Association, 2014 This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry.In addition to ...
Jon Patrick, Dung H M Nguyen
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Active machine learning for transmembrane helix prediction [PDF]
BMC Bioinformatics, 2010 Abstract Background About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in ...
Hatice U. Osmanbeyoglu +3 more
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JMIR Medical Informatics, 2021 BackgroundThe rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy.
Wael Abdelkader +7 more
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Machine Learning‐Evolutionary Algorithm Enabled Design for 4D‐Printed Active Composite Structures
Advanced Functional Materials, 2021 Active composites consisting of materials that respond differently to environmental stimuli can transform their shapes. Integrating active composites and 4D printing allows the printed structure to have a pre‐designed complex material or property ...
Xiaohao Sun +8 more
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Prediction of GPCR activity using machine learning
Computational and Structural Biotechnology Journal, 2022 GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure-activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal side-effects, it is necessary to quantitatively ...
Prakarsh Yadav +4 more
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Exploiting redundancy in large materials datasets for efficient machine learning with less data [PDF]
Nature Communications, 2023 Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to
Kangming Li +5 more
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Active learning for data streams: a survey [PDF]
Machine-mediated learning, 2023 Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in
Davide Cacciarelli, M. Kulahci
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