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Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification

机译：弱准确的基础学习者提高回归和分类的一些结果

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Wenxin Jiang;
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年度 2000
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1. On weak base hypotheses and their implications for boosting regression and classification [J] . Jiang WX. The Annals of Statistics: An Official Journal of the Institute of Mathematical Statistics . 2002,第1期

机译：关于弱基础假设及其对促进回归和分类的启示
2. Boosting regression methods based on a geometric conversion approach:Using SVMs base learners [J] . Feng Gao, Peng Kou, Lin Gao, Neurocomputing . 2013,第auga3期

机译：提高基于几何转换方法的回归方法：使用SVM基础学习者
3. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran [J] . Naghibi Seyed Amir, Pourghasemi Hamid Reza, Dixon Barnali Environmental Monitoring and Assessment . 2016,第1期

机译：在伊朗使用增强型回归树，分类和回归树以及随机森林机器学习模型的基于GIS的地下水潜力测绘
4. Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification [C] . Wenxin Jiang International Workshop on Multiple Classifier Systems . 2000

机译：一些结果对弱准确的基础学习者，用于提高回归和分类
5. Part based object detection, segmentation, and tracking by boosting simple shape feature based weak classifiers [D] . Wu, Bo 2008

机译：通过增强基于简单形状特征的弱分类器来进行基于零件的对象检测，分割和跟踪
6. The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study [O] . Anna Larsson, Johanna Berg, Mikael Gellerfors, 2021

机译：与Logistic回归相比先进的机器学习者XGBoost并没有减少预孢子创伤误解：模拟研究
7. On weak base Hypotheses and their implications for boosting regression and classification [O] . Wenxin Jiang 2002

机译：关于弱基假设及其对升压回归和分类的影响

Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification

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