Results 31 to 40 of about 2,307,608 (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
doaj +1 more source
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
doaj +1 more source
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters [PDF]
, 2019 We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper.
Chen, Yang +9 more
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QuantuMoonLight: A low-code platform to experiment with quantum machine learning
SoftwareX, 2023 Nowadays, machine learning is being used to address multiple problems in various research fields, with software engineering researchers being among the most active users of machine learning mechanisms.
Francesco Amato +18 more
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Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank [PDF]
, 2019 For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning.
Bagdanov, Andrew D. +2 more
core +2 more sources
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
semanticscholar +1 more source
Classifying Human Activities Using Machine Learning and Deep Learning Techniques
, 2023 Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smartphones or Smart Watches, which can help them manage their daily routines in a healthy way.
Uday, Sanku Satya +3 more
openaire +2 more sources
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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Risks, 2018 Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it
Udo Milkau, Jürgen Bott
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Active Learning of Nondeterministic Finite State Machines [PDF]
Mathematical Problems in Engineering, 2013 We consider the problem of learning nondeterministic finite state machines (NFSMs) from systems where their internal structures are implicit and nondeterministic. Recently, an algorithm for inferring observable NFSMs (ONFSMs), which are the potentially learnable subclass of NFSMs, has been proposed based on the hypothesis that the complete testing ...
Warawoot Pacharoen +3 more
openaire +2 more sources