Gradient Boosting

boosting approach is a kind of machine learning method, and it can establish a strong classifier from many weak classifiers. gradient boosting has been applied to motor imagery classification with higher performances.

本文使用的提升算法是一种机器学习方法，可以利用若干弱分类器建立一个强分类器，进而可以提高分类的精确度。

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The predicted errors of preserved 300 records were calculated by random forest, stochastic gradient boosting, support vector and artificial neuron network. The results show that artificial neuron network predict the most precisely.

并用随机森林、随机梯度Boosting、支持向量机和人工神经网络四种算法对预留的数据进行了预测，结果表明人工神经网络的预测误差最小。

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With Gradient Boosting Decision Tree model, adding the user behavior features including the title features, query features, click features to the literal features make accuracy increased by about 25%, recall rate increased by about 24% and F value increased by about 30%.

在梯度下降决策树模型中，加入标题、查询、点击等用户行为特征比单独使用字面特征准确率提高了约25%，召回率提高了约24%，F值提高了约30%。

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finally fused feature is input into estimator trained by gradient boosting for prediction result.

最后将融合后的特征输入到梯度boosting中得到预测结果。

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firstly, it introduces the gradient boosting theory and the no weight regression algorithm based on this theory, then it presents the experimental results of a practical problem.

首先对以损失函数梯度下降为原理的样本无权值算法进行了阐述，并给出了一个实际问题的仿真结果。

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study of nonlinear dynamics and analysis of stochastic gradient boosting in price system

价格系统的非线性动力学研究与随机梯度回归分析

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