### 电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|DATA5001

statistics-lab™ 为您的留学生涯保驾护航 在代写数据管理和数据系统Data Management and Data Systems方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据管理和数据系统Data Management and Data Systems方面经验极为丰富，各种代写数据管理和数据系统Data Management and Data Systems相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|Support Vector Machine-Decision Tree

In proposed work, hybrid SVM-based decision tree has been introduced to obtain best classification result and to speeding up the process. SVM make pattern recognition and could do data analysis as possible. Regression analysis and the classification are being carried out using Support Vector Machine. Thus the result got from applying SVM would act as a decision-making model. Support vector machine represented in short form as SVM is one among supervised learning mechanisms in computer science and the statistics. Support Vector Machine intent in analyzing the data and for recognizing the patterns. It may deal by individually with the classification and also regression analysis. Data would linearly that are separable which makes the researchers by means of identifying both hyperplanes in margin. This evaluation purely depends on the method in no points present in between and it may maximize distance among all. SVM might help in splitting the data having hyperplane and would also extend nonlinear boundaries by means of kernel trick. SVM would do classification method by correct in terms of classifying data present. It is also been described mathematically as following:
\begin{aligned} &x_{i} \cdot w+b \geq+1 \text { for } y_{i}=+1 \ &x_{i} \cdot w+b \leq-1 \text { for } y_{i}–1 \end{aligned}
Above equations may also combine in forming one set of the differences as shown below,
$$y_{i}\left(x_{i} \cdot w+b\right)-1 \geq 0 \quad \forall i$$
Thus,
$x$ denotes vector point
$w$ denotes weight parameter as vector.

## 电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|Time complexity

The system works well, thus algorithm would provide the lower complexity values and it is illustrated in Fig. $3 .$

From Fig. 4, it has been noted that comparison metric is analyzed by the existing and the proposed method by means of the time complexity. In $\mathrm{x}$-axis, algorithms are been taken and in $y$-axis time complexity value has been plotted. The existing method may provide high time complexity, while proposing system might provide low time complexity for inputting data. The proposing SVM-DT approach is used for selecting the good rules among all. At last, these rules are to be applied on train and test phase in the aim of producing highly more related data on the time series dataset. The result has proven that the introducing system would attain higher classification results with SVM-DT mechanism. Thus introduced SVM-DT is assumed as superior to previous one namely the SVM, the ARM and the SWT-IARM with ESVM algorithms (Fig. 4).
From the above draw chart, rules are generated by the existing and the proposed algorithms have been made to compared and showed. For $x$-axis, algorithms are been taken and in the $y$-axis, rule discovery value is placed. The proposing SVM-DT would provide very low number of the rules and thus it proven the superior time series classification.

In this system, time series dataset is made to evaluate by using an efficient techniques. The indexing approach is focus on increasing the similarity and the faster access. The time required for constructing data series index which evolve to prohibitive as data grows, and they might consume less amount of time for the large sizing data series. In this preprocessing has been taken place as first step by means of Kalman filtering. Then it is applicable for hybrid segmentation process by means of combining the clustering approaches and particle swarm optimization methodologies. Finally SVMDT stands for Support vector Machine-Decision Tree has been applied to carry out an effective sequence mining and thus obtains the better classification output.

In future work, a new system will develop by means of various data mining approached in terms of increasing the accuracy and reducing the time complexity as compared to this introduced system.

## 电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|Support Vector Machine-Decision Tree

$$x_{i} \cdot w+b \geq+1 \text { for } y_{i}=+1 \quad x_{i} \cdot w+b \leq-1 \text { for } y_{i}-1$$

$$y_{i}\left(x_{i} \cdot w+b\right)-1 \geq 0 \quad \forall i$$

$x$ 表示向量点
$w$ 将权重参数表示为向量。

## 广义线性模型代考

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## 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。