Showing 161 - 180 results of 5,575 for search '"machine learning"', query time: 0.06s Refine Results
  1. 161

    Modal Logic, Probability and Machine Learning Systems for Metadata Extraction by Simone Cuconato

    Published 2024-12-01
    Subjects: “…modal logic, probability, logical reasoning, machine learning systems…”
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    The advantages of lexicon-based sentiment analysis in an age of machine learning. by A Maurits van der Veen, Erik Bleich

    Published 2025-01-01
    “…Automated approaches make it possible to code near unlimited quantities of texts rapidly, replicably, and with high accuracy. Compared to machine learning and large language model (LLM) approaches, lexicon-based methods may sacrifice some in performance, but in exchange they provide generalizability and domain independence, while crucially offering the possibility of identifying gradations in sentiment. …”
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    Advancements in Image Classification: From Machine Learning to Deep Learning by Cheng Haoran

    Published 2025-01-01
    “…Subsequently, the paper provides an in-depth analysis of image classification methods based on machine learning, including traditional algorithms such as Support Vector Machine (SVM), Random Forest, and Decision Tree. …”
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    Prediction of Mg Alloy Corrosion Based on Machine Learning Models by Zhenxin Lu, Shujing Si, Keying He, Yang Ren, Shuo Li, Shuman Zhang, Yi Fu, Qi Jia, Heng Bo Jiang, Haiying Song, Mailing Hao

    Published 2022-01-01
    “…The RF algorithm offered the most accurate predictions than the other three machine learning algorithms. The input effects on corrosion potential have been investigated. …”
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  14. 174

    Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy by Jing-Mei Yin, Yang Li, Jun-Tang Xue, Guo-Wei Zong, Zhong-Ze Fang, Lang Zou

    Published 2024-01-01
    “…We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. …”
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    Predicting the thickness of shallow landslides in Switzerland using machine learning by C. Schaller, C. Schaller, L. Dorren, M. Schwarz, C. Moos, A. C. Seijmonsbergen, E. E. van Loon

    Published 2025-02-01
    “…We tested three machine learning (ML) models based on random forest (RF) models, generalised additive models (GAMs), and linear regression models (LMs). …”
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