### 机器学习代写|监督学习代考Supervised and Unsupervised learning代写|An Overview of Machine Learning

statistics-lab™ 为您的留学生涯保驾护航 在代写监督学习Supervised and Unsupervised learning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。我们的专家在代写监督学习Supervised and Unsupervised learning代写方面经验极为丰富，各种代写监督学习Supervised and Unsupervised learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 机器学习代写|监督学习代考Supervised and Unsupervised learning代写|An Overview of Machine Learning

The amount of data produced by sensors has increased explosively as a result of the advances in sensor technologies that allow engineers and scientists to quantify many processes in fine details. Because of the sheer amount and complexity of the information available, engineers and scientists now rely heavily on computers to process and analyze data. This is why machine learning has become an emerging topic of research that has been employed by an increasing number of disciplines to automate complex decision-making and problem-solving tasks. This is because the goal of machine learning is to extract knowledge from experimental data and use computers for complex decision-making, i.e. decision rules are extracted automatically from data by utilizing the speed and the robustness of the machines. As one example, the DNA microarray technology allows biologists and medical experts to measure the expressiveness of thousands of genes of a tissue sample in a single experiment. They can then identify cancerous genes in a cancer study. However, the information that is generated from the DNA microarray experiments and many other measuring devices cannot be processed or analyzed manually because of its large size and high complexity. In the case of the cancer study, the machine learning algorithm has become a valuable tool to identify the cancerous genes from the thousands of possible genes. Machine-learning techniques can be divided into three major groups based on the types of problems they can solve, namely, the supervised, semi-supervised and unsupervised learning.
The supervised learning algorithm attempts to learn the input-output relationship (dependency or function) $f(x)$ by using a training data set $\left{\mathcal{X}=\left[\mathbf{x}{i}, y{i}\right], i=1, \ldots, n\right}$ consisting of $n$ pairs $\left(\mathbf{x}{1}, y{1}\right),\left(\mathbf{x}{2}, y{2}\right), \ldots\left(\mathbf{x}{n}, y{n}\right)$, where the inputs $\mathbf{x}$ are $m$-dimensional vectors $\mathbf{x} \in \Re^{m}$ and the labels (or system responses) $y$ are discrete (e.g., Boolean) for classification problems and continuous values $(y \in \Re)$ for regression tasks. Support Vector Machines (SVMs) and Artificial Neural Network (ANN) are two of the most popular techniques in this area.

There are two types of supervised learning problems, namely, classification (pattern recognition) and the regression (function approximation) ones. In the classification problem, the training data set consists of examples from different classes. The simplest classification problem is a binary one that consists of training examples from two different classes ( $+1$ or $-1$ class). The outputs $y_{i} \in{1,-1}$ represent the class belonging (i.e. labels) of the corresponding input vectors $\mathbf{x}{i}$ in the classification. The input vectors $\mathbf{x}{i}$ consist of measurements or features that are used for differentiating examples of different classes. The learning task in classification problems is to construct classifiers that can classify previously unseen examples $\mathbf{x}_{j}$. In other words, machines have to learn from the training examples first, and then they should make complex decisions based on what they have learned. In the case of multi-class problems, several binary classifiers are built and used for predicting the labels of the unseen data, i.e. an $N$-class problem is generally broken down into $N$ binary classification problems. The classification problems can be found in many different areas, including, object recognition, handwritten recognition, text classification, disease analysis and DNA microarray studies. The term “supervised” comes from the fact that the labels of the training data act as teachers who educate the learning algorithms.

## 机器学习代写|监督学习代考Supervised and Unsupervised learning代写|Challenges in Machine Learning

Like most areas in science and engineering, machine learning requires developments in both theoretical and practical (engineering) aspects. An activity on the theoretical side is concentrated on inventing new theories as the foundations for constructing novel learning algorithms. On the other hand, by extending existing theories and inventing new techniques, researchers who work in the engineering aspects of the field try to improve the existing learning algorithms and apply them to the novel and challenging real-world problems. This book is focused on the practical aspects of SVMs, graph-based semisupervised learning algorithms and two basic unsupervised learning methods. More specifically, it aims at making these learning techniques more practical for the implementation to the real-world tasks. As a result, the primary goal of this book is aimed at developing novel algorithms and software that can solve large-scale SVMs, graph-based semi-supervised and unsupervised learning problems. Once an efficient software implementation has been obtained, the goal will be to apply these learning techniques to real-world problems and to improve their performance. Next four sections outline the original contributions of the book in solving the mentioned tasks.

## 机器学习代写|监督学习代考Supervised and Unsupervised learning代写|Solving Large-Scale SVMs

As mentioned previously, machine learning techniques allow engineers and scientists to use the power of computers to process and analyze large amounts of information. However, the amount of information generated by sensors can easily go beyond the processing power of the latest computers available. As a result, one of the mainstream research fields in learning from empirical data is to design learning algorithms that can be used in solving large-scale problems efficiently. The book is primarily aimed at developing efficient algorithms for implementing SVMs. SVMs are the latest supervised learning techniques from statistical learning theory and they have been shown to deliver state-of-the-art performance in many real-world applications [153]. The challenge of applying SVMs on huge data sets comes from the fact that the amount of computer memory required for solving the quadratic programming (QP) problem associated with SVMs increases drastically with the size of the training data set $n$ (more details can be found in Chap. 3). As a result, the book aims at providing a better solution for solving large-scale SVMs using iterative algorithms. The novel contributions presented in this book are as follows:

1. The development of Iterative Single Data Algorithm (ISDA) with the explicit bias term $b$. Such a version of ISDA has been shown to perform better (faster) than the standard SVMs learning algorithms achieving at the same time the same accuracy. These contributions are presented in Sect. $3.3$ and 3.4.
2. An efficient software implementation of the ISDA is developed. The ISDA software has been shown to be significantly faster than the well-known SVMs learning software LIBSVM [27]. These contributions are presented in Sect. 3.5.

## 机器学习代写|监督学习代考Supervised and Unsupervised learning代写|Solving Large-Scale SVMs

1. 具有显式偏置项的迭代单数据算法 (ISDA) 的开发b. 这种版本的 ISDA 已被证明比标准 SVM 学习算法表现更好（更快），同时实现相同的准确性。这些贡献在第 3 节中介绍。3.3和 3.4。
2. 开发了 ISDA 的有效软件实现。ISDA 软件已被证明比著名的支持向量机学习软件 LIBSVM [27] 快得多。这些贡献在第 3 节中介绍。3.5.

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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。