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Guest Editorial: Automated Machine Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 This special section is formed by 15 articles of outstanding quality that together comprise a snapshot of cutting edge automated machine learning (AutoML) research.
Hugo Jair Escalante +6 more
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Automated interpretable computational biology in the clinic: a framework to predict disease severity and stratify patients from clinical data [PDF]
Interdisciplinary Description of Complex Systems, 2017 We outline an automated computational and machine learning framework that predicts disease severity and stratifies patients. We apply our framework to available clinical data.
Soumya Banerjee
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PREDICTION OF TELECOM SERVICES CONSUMERS CHURN BY USING MACHINE LEARNING ALGORITHMS
Economic Review, 2022 Machine learning, or as it is also called automated learning, is a special subfield of scientific information technologies. The name "machine learning" refers to the automated detection of meaningful patterns in large data sets.
Edin Osmanbegović +2 more
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PLoS ONE, 2020 Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches.
Xubo Leng +4 more
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Sensors, 2023 Electronic manufacturing and design companies maintain test sites for a range of products. These products are designed according to the end-user requirements. The end user requirement, then, determines which of the proof of design and manufacturing tests
Atif Siddiqui +2 more
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Diagnostics, 2022 The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine ...
Sarinder Kaur Dhillon +4 more
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Adaptive Automated Machine Learning
, 2023 The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary.
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Multimodal AutoML via Representation Evolution
Machine Learning and Knowledge Extraction, 2022 With the increasing amounts of available data, learning simultaneously from different types of inputs is becoming necessary to obtain robust and well-performing models.
Blaž Škrlj, Matej Bevec, Nada Lavrač
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GenoML: Automated Machine Learning for Genomics
CoRR, 2021 GenoML is a Python package automating machine learning workflows for genomics (genetics and multi-omics) with an open science philosophy. Genomics data require significant domain expertise to clean, pre-process, harmonize and perform quality control of the data. Furthermore, tuning, validation, and interpretation involve taking into account the biology
Mary B. Makarious +16 more
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Multimodality Information Fusion for Automated Machine Translation
, 2023 Machine translation is a popular automation approach for translating texts between different languages. Although traditionally it has a strong focus on natural language, images can potentially provide an additional source of information in machine ...
Li, Lin +4 more
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