يعرض 1 - 10 نتائج من 128 نتيجة بحث عن '"boosting"', وقت الاستعلام: 0.92s تنقيح النتائج
  1. 1
    دورية أكاديمية
    Proximal boosting: Aggregating weak learners to minimize non-differentiable losses.

    المؤلفون: Fouillen, Erwan1 (AUTHOR), Boyer, Claire1 (AUTHOR), Sangnier, Maxime1 (AUTHOR) maxime.sangnier@sorbonne-universite.fr

    المصدر: Neurocomputing. Feb2023, Vol. 520, p301-319. 19p.

    مصطلحات موضوعية: *BOOSTING algorithms, *APPROXIMATION error


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  2. 2
    دورية أكاديمية
    A novel metric learning framework by exploiting global and local information.

    المؤلفون: Ren, Qiangqiang1 (AUTHOR), Yuan, Chao1 (AUTHOR), Zhao, Yifeng1 (AUTHOR), Yang, Liming1,2 (AUTHOR) cauyanglm@163.com

    المصدر: Neurocomputing. Oct2022, Vol. 507, p84-96. 13p.

    مصطلحات موضوعية: *BOOSTING algorithms, *K-nearest neighbor classification, *DISTANCE education, *PRIOR learning, *DATA structures


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  3. 3
    دورية أكاديمية
    Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation: The case of gluten bibliome.

    المؤلفون: Pérez-Pérez, Martín1,2 (AUTHOR) martiperez@uvigo.es, Ferreira, Tânia1,3,4 (AUTHOR), Lourenço, Anália1,2,5 (AUTHOR) analia@uvigo.es, Igrejas, Gilberto3,4,6 (AUTHOR) gigrejas@utad.pt, Fdez-Riverola, Florentino1,2 (AUTHOR) riverola@uvigo.es

    المصدر: Neurocomputing. May2022, Vol. 484, p223-237. 15p.

    مصطلحات موضوعية: *GLUTEN, *BOOSTING algorithms, *TEXT mining, *INFORMATION resources, *AUTOMATIC classification, *EXTRACTION techniques, *ONTOLOGIES (Information retrieval)


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  4. 4
    دورية أكاديمية
    Selective ensemble of classifiers trained on selective samples.

    المؤلفون: M. Mohammed, Amgad1,2 (AUTHOR) amgad.elsayed@deusto.es, Onieva, Enrique1 (AUTHOR) enrique.onieva@deusto.es, Woźniak, Michał3 (AUTHOR) michal.wozniak@pwr.edu.pl

    المصدر: Neurocomputing. Apr2022, Vol. 482, p197-211. 15p.

    مصطلحات موضوعية: *BOOSTING algorithms, *RANDOM forest algorithms, *ALGORITHMS, *MACHINE learning, *KALMAN filtering


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  5. 5
    دورية أكاديمية
    Entropy-aware self-training for graph convolutional networks.

    المؤلفون: Zhao, Gongpei1 (AUTHOR), Wang, Tao1 (AUTHOR) twang@bjtu.edu.cn, Li, Yidong1 (AUTHOR), Jin, Yi1 (AUTHOR), Lang, Congyan1 (AUTHOR)

    المصدر: Neurocomputing. Nov2021, Vol. 464, p394-407. 14p.

    مصطلحات موضوعية: *BOOSTING algorithms, *RANDOM walks, *ALGORITHMS


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  6. 6
    دورية أكاديمية
    AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning.

    المؤلفون: Taherkhani, Aboozar1 (AUTHOR) aboozar.taherkhani@dmu.ac.uk, Cosma, Georgina2 (AUTHOR), McGinnity, T.M.3,4 (AUTHOR)

    المصدر: Neurocomputing. Sep2020, Vol. 404, p351-366. 16p.

    مصطلحات موضوعية: *CONVOLUTIONAL neural networks, *BOOSTING algorithms, *BIG data, *REINFORCEMENT learning, *WEIGHT training, *RETINAL blood vessels


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  7. 7
    دورية أكاديمية
    Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble.

    المؤلفون: Fdez-Díaz, Laura1 (AUTHOR) laura.fernandezdiaz@arcelormittal.com, Quevedo, José Ramón2 (AUTHOR) quevedo@uniovi.es, Montañés, Elena1,2 (AUTHOR) montaneselena@uniovi.es

    المصدر: Neurocomputing. Sep2023, Vol. 551, pN.PAG-N.PAG. 1p.

    مصطلحات موضوعية: *MACHINE learning, *BOOSTING algorithms, *MULTICOLLINEARITY, *MATHEMATICAL regularization, *PREDICTION models, *COMPUTER performance


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  8. 8
    دورية أكاديمية
    MSSBoost: A new multiclass boosting to semi-supervised learning.

    المؤلفون: Tanha, Jafar1,2 Tanha@utabriz.ac.ir

    المصدر: Neurocomputing. Nov2018, Vol. 314, p251-266. 16p.

    مصطلحات موضوعية: *BOOSTING algorithms, *SUPERVISED learning, *LOGICAL prediction, *DISCONTINUOUS functions, *MATHEMATICAL regularization


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  9. 9
    دورية أكاديمية
    Boosting image sentiment analysis with visual attention.

    المؤلفون: Song, Kaikai1, Yao, Ting2, Ling, Qiang1 qling@ustc.edu.cn, Mei, Tao3

    المصدر: Neurocomputing. Oct2018, Vol. 312, p218-228. 11p.

    مصطلحات موضوعية: *BOOSTING algorithms, *SENTIMENT analysis, *CONTEXTUAL analysis, *POLARITY (Physics), *ARTIFICIAL neural networks


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  10. 10
    دورية أكاديمية
    Deep learning for Covid-19 forecasting: State-of-the-art review.

    المؤلفون: Kamalov, F., Rajab, K., Cherukuri, A. K., Elnagar, A., Safaraliev, M.

    المصدر: Neurocomputing

    مصطلحات موضوعية: CNN, COVID-19, DEEP LEARNING, FORECASTING, GNN, LSTM, MLP, SURVEY, LEARNING SYSTEMS, LONG SHORT-TERM MEMORY, 'CURRENT, GOOGLE SCHOLAR, LEARNING METHODS, MACHINE LEARNING METHODS, STATE-OF-THE ART REVIEWS, ARTICLE, CONVOLUTIONAL NEURAL NETWORK, CORONAVIRUS DISEASE 2019, GRADIENT BOOSTING, GRAPH NEURAL NETWORK, HUMAN, K NEAREST NEIGHBOR, LONG SHORT TERM MEMORY NETWORK, MATHEMATICAL MODEL, MULTILAYER PERCEPTRON, RANDOM FOREST, RECURRENT NEURAL NETWORK, SUPPORT VECTOR MACHINE, SUSCEPTIBLE EXPOSED INFECTIOUS RECOVERED MODEL, TAXONOMY

    وصف الملف: application/pdf

    العلاقة: Kamalov, F, Rajab, K, Cherukuri, AK, Elnagar, A & Safaraliev, M 2022, 'Deep learning for Covid-19 forecasting: State-of-the-art review', Neurocomputing, Том. 511, стр. 142-154. https://doi.org/10.1016/j.neucom.2022.09.005Test; Kamalov, F., Rajab, K., Cherukuri, A. K., Elnagar, A., & Safaraliev, M. (2022). Deep learning for Covid-19 forecasting: State-of-the-art review. Neurocomputing, 511, 142-154. https://doi.org/10.1016/j.neucom.2022.09.005Test; Final; All Open Access; Green Open Access; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454152Test; http://elar.urfu.ru/handle/10995/132485Test; 85138086201; 871948700012

    الإتاحة: https://doi.org/10.1016/j.neucom.2022.09.005Test
    http://elar.urfu.ru/handle/10995/132485Test
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454152Test

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