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Using Automated Machine Learning for Spatial Prediction—The Heshan Soil Subgroups Case Study
LandRecently, numerous spatial prediction methods with diverse characteristics have been developed. Selecting an appropriate spatial prediction method, along with its data preprocessing and parameter settings, presents a challenging task for many users ...
Peng Liang, Cheng-Zhi Qin, A-Xing Zhu
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An Empirical Review of Automated Machine Learning [PDF]
Computers, 2021 In recent years, Automated Machine Learning (AutoML) has become increasingly important in Computer Science due to the valuable potential it offers. This is testified by the high number of works published in the academic field and the significant efforts ...
Lorenzo Vaccaro +2 more
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Techniques for Automated Machine Learning [PDF]
ACM SIGKDD Explorations Newsletter, 2021 Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a problem description, its task type, and datasets. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive ...
Yi-Wei Chen, Qingquan Song, Xia Hu 0001
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Naive automated machine learning
Machine Learning, 2022 Abstract An essential task of automated machine learning ( $$\text {AutoML}$$ AutoML ) is the problem of ...
Felix Mohr, Marcel Wever
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A foundation for machine learning in design [PDF]
, 1998 This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be ...
Duffy, Alex H.B., Sim, Siang Kok
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Automating In-Network Machine Learning
CoRR, 2022 Using programmable network devices to aid in-network machine learning has been the focus of significant research. However, most of the research was of a limited scope, providing a proof of concept or describing a closed-source algorithm. To date, no general solution has been provided for mapping machine learning algorithms to programmable network ...
Changgang Zheng +6 more
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Machine Learning in Automation
at - Automatisierungstechnik, 2018 Machine learning (ML) refers to the automated detection of meaningful patterns in a given data. It can appear in many guises. It involves programming computers to perform tasks using example data or past experience. It is capable of accelerating the pace of automation. As tasks are offloaded to automation, humans can address other challenges.
Jürgen Beyerer, Oliver Niggemann
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Automated Adaptation Strategies for Stream Learning [PDF]
, 2021 Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model ...
Gabrys, Bogdan +9 more
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Automated Machine Learning for COVID-19 Forecasting
IEEE Access, 2022 In the context of the current COVID-19 pandemic, various sophisticated epidemic and machine learning models have been used for forecasting. These models, however, rely on carefully selected architectures and detailed data that is often only available for
Jaco Tetteroo +2 more
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Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning [PDF]
, 2023 A computational approach by an implementation of the principle component analysis (PCA) with K-means and Gaussian mixture (GM) clustering methods from machine learning algorithms to identify structural and dynamical heterogeneities of supercooled liquids
Song, Xueyu, Nguyen, Viet
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