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Information Theory for Data Science

Suh, Changho

2023

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機械学習における深層学習：基礎と応用例

材料, 2020/09/15, Vol.69(9), pp.633-639

中島, 智晴

2020

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Machine Learning

1st Edition 2021, 2021

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Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection

Organizational research methods, 2024-01, Vol.27 (1), p.88-113

2024

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Task‐induced brain functional connectivity as a representation of schema for mediating unsupervised and supervised learning dynamics in language acquisition

Brain and behavior, 2021-06, Vol.11 (6), p.e02157-n/a

2021

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A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

Scientific reports, 2024-04, Vol.14 (1), p.9782-9782

2024

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Deep learning intravoxel incoherent motion modeling: Exploring the impact of training features and learning strategies

Magnetic resonance in medicine, 2023-07, Vol.90 (1), p.312-328

2023

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Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine Learning

World neurosurgery, 2024-03, Vol.183, p.e953-e962

2024

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Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis

PloS one, 2021-12, Vol.16 (12), p.e0261511-e0261511

2021

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Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry

International journal of molecular sciences, 2022-03, Vol.23 (5), p.2797

Polanski, Jaroslaw

2022

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More Is Better: Recent Progress in Multi-Omics Data Integration Methods

Frontiers in genetics, 2017, Vol.8, p.84-84

2017

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Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation

Genes, 2020-07, Vol.11 (7), p.792

2020

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Using machine learning approaches for multi-omics data analysis: A review

Biotechnology advances, 2021-07, Vol.49, p.107739-107739, Article 107739

2021

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Applications of machine learning in animal behaviour studies

Animal behaviour, 2017-02, Vol.124, p.203-220

2017

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Machine Learning for Sociology

Annual review of sociology, 2019-07, Vol.45 (1), p.27-45

2019

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Weak Augmentation Guided Relational Self-Supervised Learning

IEEE transactions on pattern analysis and machine intelligence, 2024-05, Vol.PP, p.1-15

2024

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IEEE_Xplore

Survival prediction model with lion optimization for Hepatitis-B patients using supervised and unsupervised learning algorithm

Measurement. Sensors, 2024-04, Vol.32, p.100958, Article 100958

2024

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Coronavirus disease (COVID-19) cases analysis using machine-learning applications

Applied nanoscience, 2023, Vol.13 (3), p.2013-2025

2023

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Machine learning and applications in microbiology

FEMS microbiology reviews, 2021-09, Vol.45 (5), p.1

2021

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Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications

Journal of critical care, 2024-10, Vol.83, p.154815-154815, Article 154815

2024

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Intelligent detection of loose fasteners in railway tracks using distributed acoustic sensing and machine learning

Engineering applications of artificial intelligence, 2024-08, Vol.134, Article 108684

2024

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Deep Neural Networks meet computation offloading in mobile edge networks: Applications, taxonomy, and open issues

Journal of network and computer applications, 2024-06, Vol.226, Article 103886

2024

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Unsupervised Learning for Solving AC Optimal Power Flows: Design, Analysis, and Experiment

IEEE transactions on power systems, 2024-03, p.1-13

2024

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IEEE_Xplore

Learning relevant features of data with multi-scale tensor networks

Quantum Science and Technology, 2018-07, Vol.3 (3), p.34003

Stoudenmire, E Miles

2018

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Machine Learning für Softwareentwickler: Von der Python-Codezeile zur Deep-Learning-Anwendung

1, 2020

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Information Theory for Data Science

機械学習における深層学習：基礎と応用例

Machine Learning

Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection

Task‐induced brain functional connectivity as a representation of schema for mediating unsupervised and supervised learning dynamics in language acquisition

A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

Deep learning intravoxel incoherent motion modeling: Exploring the impact of training features and learning strategies

Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine Learning

Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis

Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry

More Is Better: Recent Progress in Multi-Omics Data Integration Methods

Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation

Using machine learning approaches for multi-omics data analysis: A review

Applications of machine learning in animal behaviour studies

Machine Learning for Sociology

Weak Augmentation Guided Relational Self-Supervised Learning

Survival prediction model with lion optimization for Hepatitis-B patients using supervised and unsupervised learning algorithm

Coronavirus disease (COVID-19) cases analysis using machine-learning applications

Machine learning and applications in microbiology

Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications

Intelligent detection of loose fasteners in railway tracks using distributed acoustic sensing and machine learning

Deep Neural Networks meet computation offloading in mobile edge networks: Applications, taxonomy, and open issues

Unsupervised Learning for Solving AC Optimal Power Flows: Design, Analysis, and Experiment

Learning relevant features of data with multi-scale tensor networks

Machine Learning für Softwareentwickler: Von der Python-Codezeile zur Deep-Learning-Anwendung

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