POINTS OF SIGNIFICANCE Ensemble methods: bagging and random forests

Altman Naomi; Krzywinski Martin

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POINTS OF SIGNIFICANCE Ensemble methods: bagging and random forests

机译：重要性乐合容方法：袋装和随机森林

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来源
《Nature methods》 |2017年第10期|共2页
作者
Altman Naomi; Krzywinski Martin;
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作者单位

Penn State Univ Stat University Pk PA 16802 USA;

Canadas Michael Smith Genome Sci Ctr Vancouver BC Canada;

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正文语种 eng
中图分类生物科学;
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1. POINTS OF SIGNIFICANCE Ensemble methods: bagging and random forests [J] . Altman Naomi, Krzywinski Martin Nature methods . 2017,第10期

机译：重要性乐合容方法：袋装和随机森林
2. Random forest and rotation forest ensemble methods for classification of epileptic EEG signals based on improved 1D-LBP feature extraction [J] . Geran Malek Amirreza, Mansoori Mojtaba, Omranpour Hesam International journal of imaging systems and technology . 2021,第1期

机译：基于改进的1D-LBP特征提取的癫痫脑电图信号分类随机森林和旋转林合成方法
3. Flat random forest: a new ensemble learning method towards better training efficiency and adaptive model size to deep forest [J] . Liu Peng, Wang Xuekui, Yin Liangfei, International journal of machine learning and cybernetics . 2020,第11期

机译：平面随机森林：一种新的培训效率和自适应模型大小到深林的新集合学习方法
4. Combining ensemble methods of Bagging, Subagging and Random Subspace for phoneme recognition [C] . Abir Bousmina, Chiraz Jlassi, Najet Arous International Conference on Advanced Technologies for Signal and Image Processing . 2016

机译：Bagging，Subagging和Random子空间的组合集成方法用于音素识别
5. Randomness in tree ensemble methods. [D] . Elias, Joran. 2009

机译：树集合方法中的随机性。
6. Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning [O] . Gaowei Xu, Min Liu, Zhuofu Jiang, 2019

机译：基于深度卷积神经网络和随机森林集成学习的轴承故障诊断方法
7. Evaluation of Random Forest and Ensemble Methods at Predicting Complications Following Cardiac Surgery [O] . Linda Lapp, Matt-Mouley Bouamrane, Kimberley Kavanagh, 2019

机译：随机森林和集合方法评价在心脏手术后试验中的复杂性

POINTS OF SIGNIFICANCE Ensemble methods: bagging and random forests

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