What is the translation of " BOOSTING " in English?

• boosting

Examples of using Boosting in Chinese and their translations into English

Boosting由三个简单步骤组成：.
Boosting consists of three simple steps:.

这是理解boosting的最佳起点。
It is the best starting point for understanding boosting.

梯度Boosting以顺序的方式建立模型。
Gradient Boosting builds the model in a sequential way.

加入随机因子，例如采用bagging和boosting方法.
Use ensemble methods such as bagging and boosting.

然后有boosting，randomforests，等等，直到回到神经网络。
Then there was boosting, random forests, and so on, till the return of neural networks.

但它们通常被用于诸如随机森林或gradientboosting之类的组合中。
But they are most often used in compositions such as Random Forest or Gradient boosting.

基本上，adaboosting是第一个为二进制分类开发的真正成功的增强算法。
Basically, Ada Boosting was the first really successful boosting algorithm developed for binary classification.

具有线性参数化的非线性分类器：基函数方法，boosting，支持向量机。
Non-linear classifiers with linear parameterizations: basis-function methods, boosting, Support Vector Machines.

Boosting是一个顺序过程，每个后续模型都会尝试纠正先前模型的错误。
Boosting is a sequential process, where each subsequent model attempts to correct the errors of the previous model.

然而，当不断进行boosting迭代，误差率将稳定下降，直到400次迭代之后达到5.8%。
However, as boosting iterations proceed the error rate steadily decreases, reaching 5.8% after 400 iterations.

图10.2.（10.2）的模拟数据：对stumps进行boosting的测试误差率作为迭代次数的函数。
Simulated data(10.2): test error rate for boosting with stumps, as a function of the number of iterations.

众所周知，FortniteBoosting是一项艰苦的工作，可能需要数天或数周才能实现您的目标。
As you all know, Maplestory 2 Dungeon Boosting is hard work and can take days or weeks to achieve your goal.

结果，你建了5个GBM（GradientBoostedModels），想着boosting算法会显示魔力。
As a result, you build 5 GBM(gradient boosting model) models, thinking a boosting algorithm would do the magic.

在Boosting中，树是按顺序构建的，这样每个后续树的目的是减少前一棵树的错误。
In boosting, the trees are built sequentially such that each subsequent tree aims to reduce the errors of the previous tree.

在本教程中，我们将了解两种最常用的算法，即GradientBoosting（GBM）和XGboost。
In this tutorial, we will learn about the two most commonly used algorithms i.e. Gradient Boosting(GBM) and XGboost.

决策树、随机森林、gradientboosting等方法被广泛用于各种数据学科问题中。
Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems.

在通过boosting达到最大精度的阶段，残差看起来是随机分布的，没有任何模式。
At the stage where maximum accuracy is reached by boosting, the residuals appear to be randomly distributed without any pattern.

下述算法描述了使用最广泛的，称为AdaBoost的boosting算法，它代表着自适应增强。
The algorithm below describes the most widely used form of boosting algorithm called AdaBoost, which stands for adaptive boosting.

这些boosting算法在Kaggle，AVHackthon,CrowdAnalytix等数据科学竞赛中有出色发挥。
These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, CrowdAnalytix.

原始的集成方法是贝叶斯平均，但是最近的算法包括纠错输出编码、Bagging和Boosting。
The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting.

另一方面，boosting是一个连续的集合，每个模型的建立都是基于纠正前一个模型的错误分类。
On the other hand, boosting is a sequential ensemble where each model is built based on correcting the misclassifications of the previous model.

下面的算法阐述了最广泛使用的boosting算法形式，称为AdaBoost，是adaptiveboosting的缩写。
The algorithm below describes the most widely used form of boosting algorithm called AdaBoost, which stands for adaptive boosting.

Boosting是一种计算输出的方法，即使用多个不同的模型，然后使用加权平均的方法对结果取平均值。
Boosting is an approach to calculate the output using several different models and then average the result using a weighted average approach.

在某些情况下，Boosting已被证明比Bagging可以得到更好的准确率，不过它也更倾向于对训练数据过拟合。
In some cases, boosting has been shown to yield better accuracy than bagging, but it also tends to be more likely to over-fit the training data.

Boosting是基于Kearns和Valiant（1988,1989）提出的问题：一组弱学习器能创造一个强大的学习器吗？?
Boosting is based on the question posed by Kearns and Valiant(1988, 1989):"Can a set of weak learners create a single strong learner?

Bagging使用复杂的基础模型，试图“平滑”他们的预测，而Boosting使用简单的基础模型，并试图“提高”他们的总复杂度。
Bagging uses complex base models and tries to smooth out their predictions while boosting uses simple base models and tries to boost its aggregate complexity.

Boosting实际上是一个学习算法的集合，它结合了几个基本估计量的预测，以便比单个估计量提高坚固性。
Boosting is actually an ensemble of learning algorithms which combines the prediction of several base estimators in order to improve robustness over a single estimator.

最初的集成方法是贝叶斯平均法(Bayesianaveraging)，但是最近的算法集还包括了纠错输出编码(error-correctingoutputcoding)，bagging和boosting.
The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting.