Results 31 to 40 of about 1,171,833 (351)
An online deep extreme learning machine based on forgetting mechanism
Dianzi Jishu Yingyong, 2018 The development of deep learning promotes the development of deep online learning, and online learning tends to have strong effectiveness. Based on the principle of online extreme learning machine and the principle of autoencoder of deep extreme learning
Liu Buzhong
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Nuclear liquid-gas phase transition with machine learning
Physical Review Research, 2020 Machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ machine-learning techniques to study the nuclear liquid-gas phase transition.
Rui Wang +6 more
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A dataset of mentorship in bioscience with semantic and demographic estimations
Scientific Data, 2022 Measurement(s) Semantics • Gender • Race Technology Type(s) unsupervised machine learning • supervised machine learning Factor Type(s) FirstName • FullName • MAGPaperID • DOI • PMID • TFIDFVector • SpecterVector • PID • NeighborPID • SpecterDistance ...
Qing Ke +4 more
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Tensor networks for unsupervised machine learning
Physical Review E, 2023 Modeling the joint distribution of high-dimensional data is a central task in unsupervised machine learning. In recent years, many interests have been attracted to developing learning models based on tensor networks, which have the advantages of a principle understanding of the expressive power using entanglement properties, and as a bridge connecting ...
Jing Liu +3 more
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A new approach to studying professionally important qualities of operator-manipulators based on machine learning [PDF]
E3S Web of ConferencesThis paper proposes a new approach to the evaluation of an operator-manipulator’s readiness for their labour activity. This approach is based on an unsupervised machine learning method, namely on clustering.
Petukhov Igor +4 more
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Machine learning and earthquake forecasting—next steps
Nature Communications, 2021 A new generation of earthquake catalogs developed through supervised machine-learning illuminates earthquake activity with unprecedented detail. Application of unsupervised machine learning to analyze the more complete expression of seismicity in these ...
Gregory C. Beroza +2 more
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Synthetic shear sonic log generation utilizing hybrid machine learning techniques
Artificial Intelligence in Geosciences, 2022 Compressional and shear sonic logs (DTC and DTS, respectively) are one of the effective means for determining petrophysical/geomechanical properties. However, the DTS log has limited availability mainly due to high acquisition costs.
Jongkook Kim
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Estimating extinction using unsupervised machine learning [PDF]
Astronomy & Astrophysics, 2017 Dust extinction is the most robust tracer of the gas distribution in the interstellar medium, but measuring extinction is limited by the systematic uncertainties involved in estimating the intrinsic colors to background stars. In this paper we present a new technique, PNICER, that estimates intrinsic colors and extinction for individual stars using ...
Stefan Meingast +2 more
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MACHINE LEARNING IN BIG LIDAR DATA: A REVIEW [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Machine Learning used to refer as one Artificial Intelligence subsection that perform self-learning computational algorithms either supervised learning or unsupervised learning tasks.
S. S. Teri, I. A. Musliman
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Special Issue on Supervised and Unsupervised Classification Algorithms—Foreword from Guest Editors
Algorithms, 2023 Supervised and unsupervised classification algorithms are the two main branches of machine learning [...]
Laura Antonelli +1 more
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