Results 11 to 20 of about 2,291,078 (312)
Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction
Sensors, 2022 The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system.
Ibrahim M. El-Hasnony +3 more
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Transductive confidence machine for active learning [PDF]
Proceedings of the International Joint Conference on Neural Networks, 2003., 2004 This paper describes a novel active learning strategy using universal p-value measures of confidence based on algorithmic randomness, and transconductive inference. The early stopping criterion for active learning is based on the bias-variance tradeoff for classification.
Shen-Shyang Ho, Harry Wechsler
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Digital Chemical Engineering, 2022 In this work, we develop a model reduction method using sensitivity analysis and active learning to improve the computational efficiency of machine learning modeling of nonlinear processes.
Tianyi Zhao, Yingzhe Zheng, Zhe Wu
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Streaming Machine Learning and Online Active Learning for Automated Visual Inspection.
IFAC-PapersOnLine, 2022 Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided ...
Jože M. Rožanec +4 more
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Active Learning for Neural Machine Translation
SSRN Electronic Journal, 2023 The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge ...
Neeraj Vashistha +2 more
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Visual Informatics, 2019 Supervised machine learning techniques require labelled multivariate training datasets. Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations. Using appropriate techniques,
Mohammad Chegini +5 more
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Universal activation function for machine learning [PDF]
Scientific Reports, 2021 AbstractThis article proposes a universal activation function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems. For any given problem, the gradient descent algorithms are able to evolve the UAF to a suitable activation function by tuning the UAF’s parameters.
Brosnan Yuen +3 more
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Machine learning forecasting of active nematics [PDF]
Soft Matter, 2021 Our model is unrolled to map an input orientation sequence (from time t-8 to t-1) to an output one (t,t + 1…) with trajectray tracing. Cyan labels are −1/2 defect while purple ones are +1/2.
Zhengyang Zhou +8 more
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ACTIVE LEARNING ON LARGE HYPERSPECTRAL DATASETS: A PREPROCESSING METHOD [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Machine learning algorithms demonstrated promising results for hyperspectral semantic segmentation. However, they strongly rely on the quality of training datasets.
R. Thoreau +5 more
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A Classification Method of Agricultural News Text Based on BERT and Deep Active Learning [PDF]
Nongye tushu qingbao xuebao, 2022 [Purpose/Significance] At present, most of the training models used in the research of news classification are non-active learning. There are common problems about these models, including data cannot be labeled immediately and the labeling cost is too ...
SHI Yunlai, CUI Yunpeng, DU Zhigang
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