计算机代写|机器学习代写machine learning代考|COMP5318

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine 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。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning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

计算机代写|机器学习代写machine learning代考|What Is Special About Learning from Text

Most machine learning applications in the text domain work with the bag-of-words representation in which the words are treated as dimensions with values corresponding to word frequencies. A data set corresponds to a collection of documents, which is also referred to as a corpus. The complete and distinct set of words used to define the corpus is also referred to as the lexicon. Dimensions are also referred to as terms or features. Some applications of text work with a binary representation in which the presence of a term in a document corresponds to a value of 1 , and 0 , otherwise. Other applications use a normalized function of the word frequencies as the values of the dimensions. In each of these cases, the dimensionality of data is very large, and may be of the order of $10^5$ or even $10^6$. Furthermore, most values of the dimensions are $0 \mathrm{~s}$, and only a few dimensions take on positive values. In other words, text is a high-dimensional, sparse, and non-negative representation.

These properties of text create both challenges and opportunities. The sparsity of text implies that the positive word frequencies are more informative than the zeros. There is also wide variation in the relative frequencies of words, which leads to differential importance of the different words in mining applications. For example, a commonly occurring word like “the” is often less significant and needs to be down-weighted (or completely removed) with normalization. In other words, it is often more important to statistically normalize the relative importance of the dimensions (based on frequency of presence) compared to traditional multidimensional data. One also needs to normalize for the varying lengths of different documents while computing distances between them. Furthermore, although most multidimensional mining methods can be generalized to text, the sparsity of the representation has an impact on the relative effectiveness of different types of mining and learning methods. For example, linear support-vector machines are relatively effective on sparse representations, whereas methods like decision trees need to be designed and tuned with some caution to enable their accurate use. All these observations suggest that the sparsity of text can either be a blessing or a curse depending on the methodology at hand. In fact, some techniques such as sparse coding sometimes convert non-textual data to text-like representations in order to enable efficient and effective learning methods like support-vector machines [405].

计算机代写|机器学习代写machine learning代考|Analytical Models for Text

The section will provide a comprehensive overview of text mining algorithms and applications. The next chapter of this book primarily focuses on data preparation and similarity computation. Issues related to preprocessing issues of data representation are also discussed in this chapter. Aside from the first two introductory chapters, the topics covered in this book fall into three primary categories:

1. Fundamental mining applications: Many data mining applications like matrix factorization, clustering, and classification, can he used for any type of multidimensional data. Nevertheless, the uses of these methods in the text domain has specialized characteristics. These represent the core building blocks of the vast majority of text mining applications. Chapters 3 through 8 will discuss core data mining methods. The interaction of text with other data types will be covered in Chapter 8 .
2. Information retrieval and ranking: Many aspects of information retrieval and ranking are closely related to text mining. For example, ranking methods like ranking SVM and link-based ranking are often used in text mining applications. Chapter 9 will provide an overview of information retrieval methods from the point of view of text. mining.
3. Sequence- and natural language-centric text mining: Although multidimensional mining methods can be used for basic applications, the true power of mining text can be leveraged in more complex applications by treating text as sequences. Chapters 10 through 16 will discuss these advanced topics like sequence embedding, neural learning, information extraction, summarization, opinion mining, text segmentation, and event extraction. Many of these methods are closely related to natural language processing. Although this book is not focused on natural language processing, the basic building blocks of natural language processing will be used as off-the-shelf tools for text mining applications.

In the following, we will provide an overview of the different text mining models covered in this book. In cases where the multidimensional representation of text is used for mining purposes, it is relatively easy to use a consistent notation. In such cases, we assume that a document corpus with $n$ documents and $d$ different terms can be represented as a sparse $n \times d$ document-term matrix, which is typically very sparse. The $i$ th row of $D$ is represented by the $d$-dimensional row vector $\overline{X_i}$. One can also represent a document corpus as a set of these $d$-dimensional vectors, which is denoted by $\mathcal{D}=\left[\bar{X}_1 \ldots \bar{X}_n\right]$. This terminology will be used consistently throughout the book. Many information retrieval books prefer the use of a term-document matrix, which is the transpose of the document-term matrix and the rows correspond to the frequencies of terms. However, using a document-term matrix, in which data instances are rows, is consistent with the notations used in books on multidimensional data mining and machine learning. Therefore, we have chosen to use a document-term matrix in order to consistent with the broader literature on machine learning.

Much of the book will be devoted to data mining and machine learning rather than the database management issues of information retrieval. Nevertheless, there is some overlap between the two areas, as they are both related to problems of ranking and search engines. Therefore, a comprehensive chapter is devoted to information retrieval and search engines. Throughout this book, we will use the term “learning algorithm” as a broad umbrella term to describe any algorithm that discovers patterns from the data or discovers how such patterns may be used for predicting specific values in the data.

机器学习代考

计算机代写|机器学习代写machine learning代考|Analytical Models for Text

1. 基础挖掘应用：许多数据挖掘应用，如矩阵分解、聚类和分类，可以用于任何类型的多维数据。然而，这些方法在文本域中的使用具有特殊性。这些代表了绝大多数文本挖掘应用程序的核心构建块。第 3 章到第 8 章将讨论核心数据挖掘方法。文本与其他数据类型的交互将在第 8 章介绍。
2. 信息检索和排序：信息检索和排序的许多方面都与文本挖掘密切相关。例如，排序 SVM 和基于链接的排序等排序方法经常用于文本挖掘应用程序。第 9 章将从文本的角度概述信息检索方法。矿业。
3. 以序列和自然语言为中心的文本挖掘：虽然多维挖掘方法可用于基本应用程序，但通过将文本视为序列，可以在更复杂的应用程序中利用挖掘文本的真正力量。第 10 章到第 16 章将讨论这些高级主题，如序列嵌入、神经学习、信息提取、摘要、意见挖掘、文本分割和事件提取。其中许多方法与自然语言处理密切相关。虽然本书的重点不是自然语言处理，但自然语言处理的基本构建块将用作文本挖掘应用程序的现成工具。

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。