Posted on Categories:CS代写, Machine Learning, 机器学习, 计算机代写

# 计算机代写|机器学习代写Machine Learning代考|COMP4702 Individual and Ensemble

avatest™

## avatest™帮您通过考试

avatest™的各个学科专家已帮了学生顺利通过达上千场考试。我们保证您快速准时完成各时长和类型的考试，包括in class、take home、online、proctor。写手整理各样的资源来或按照您学校的资料教您，创造模拟试题，提供所有的问题例子，以保证您在真实考试中取得的通过率是85%以上。如果您有即将到来的每周、季考、期中或期末考试，我们都能帮助您！

•最快12小时交付

•200+ 英语母语导师

•70分以下全额退款

## 计算机代写|机器学习代写Machine Learning代考|Individual and Ensemble

Ensemble learning, also known as multiple classifier system and committee-based learning, trains and combines multiple learners to solve a learning problem.

As shown in $\boldsymbol{-}$ Figure 8.1, the typical workflow of ensemble learning is training a set of individual learners first and then combining them via some strategies, where an individual learner is usually trained by an existing learning algorithm, such as the $\mathrm{C} 4.5$ algorithm and the BP neural network algorithm. An ensemble is said to be homogeneous if all individual learners are of the same type, e.g., a “decision tree ensemble” contains only decision trees, while a “neural network ensemble” contains only neural networks. For homogeneous ensembles, the individual learners are called base learners, and the corresponding learning algorithms are called base learning algorithms. In contrast, a heterogeneous ensemble contains different individual learners and learning algorithms, and there is no single base learner or base learning algorithm. For heterogeneous ensembles, the simply individual learners.

By combining multiple learners, the generalization ability of an ensemble is often much stronger than that of an individual learner, and this is especially true for weak learners. Therefore, theoretical studies on ensemble learning often focus on weak learners, and hence base learners are sometimes called weak learners. In practice, however, despite that an ensemble of weak learners can theoretically obtain good performance, people still prefer strong learners for some reasons, such as reducing the number of individual learners and reusing existing knowledge about the strong learners.

## 计算机代写|机器学习代写Machine Learning代考|Boosting

Boosting is a family of algorithms that convert weak learners to strong learners. Boosting algorithms start with training a base learner and then adjust the distribution of the training samples according to the result of the base learner such that incorrectly classified samples will receive more attention by subsequent base learners. After training the first base learner, the second base learner is trained with the adjusted training samples, and the result is used to adjust the training sample distribution again. Such a process repeats until the number of base learners reaches a predefined value $T$, and finally, these base learners are weighted and combined.

The most well-known Boosting algorithm is AdaBoost (Freund and Schapire 1997), as shown in $\boldsymbol{- 1}$ Agorithm 8.1, where $y_i \in{-1,+1}$ and $f$ is the ground-truth function.

There are multiple ways to derive the AdaBoost algorithm, but one that is easy to understand is based on the additive model, that is, using the linear combination of base learners
$$H(\boldsymbol{x})=\sum_{t=1}^T \alpha_t h_t(\boldsymbol{x})$$
to minimize the exponential loss function (Friedman et al. 2000)

## 计算机代写|机器学习代写Machine Learning代考|Boosting

Boosting 是一系列将弱学习器转换为强学习器的算法。Boosting 算法从训练一个其学习器开始，然后根据基学习器的结果调整 训练样本的分布，使错误分类的样本受到后续基学习器的更多关注。训练完第一个基学习器后，用调整后的训练样本训练第二个基 学习器，结果再次用于调整川拣样本分布。重笣这样的过程，直到其础学习者的数量达到预定义的值 $T$ ，最后对这些其础学习器进 行加权和组合。

$$H(\boldsymbol{x})=\sum_{t=1}^T \alpha_t h_t(\boldsymbol{x})$$

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。