### 计算机代写|机器学习代写machine learning代考|COMP30027

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|Text Preprocessing and Similarity Computation

Text preprocessing is required to convert the unstructured format into a structured and multidimensional representation. Text often co-occurs with a lot of extraneous data such as tags, anchor text, and other irrelevant features. Furthermore, different words have different significance in the text domain. For example, commonly occurring words such as “a,” “an,”

and “the,” have little significance for text mining purposes. In many cases, words are variants of one another because of the choice of tense or plurality. Some words are simply misspellings. The process of converting a character sequence into a sequence of words (or tokens) is referred to as tokenization. Note that each occurrence of a word in a document is a token, even if it occurs more than once in the document. Therefore, the occurrence of the same word three times will create three corresponding tokens. The process of tokenization often requires a substantial amount of domain knowledge about the specific language at hand, because the word boundaries have ambiguities caused by vagaries of punctuation in different languages.
Some common steps for preprocessing raw text are as follows:

1. Text extraction: In cases where the source of the text is the Web, it occurs in combination with various other types of data such as anchors, tags, and so on. Furthermore, in the Web-centric setting, a specific page may contain a (useful) primary block and other blocks that contain advertisements or unrelated content. Extracting the useful text from the primary block is important for high-quality mining. These types of settings require specialized parsing and extraction techniques.
2. Stop-word removal: Stop words are commonly occurring words that have little discriminative power for the mining process. Common pronouns, articles, and prepositions are considered stop words. Such words need to be removed to improve the mining process.
3. Stemming, case-folding, and punctuation: Words with common roots are consolidated into a single representative. For example, words like “sinking” and “sank” are consolidated into the single token “sink.” The case (i.e., capitalization) of the first alphabet of a word may or may not be important to its semantic interpretation. For example, the word “Rose” might either be a flower or the name of a person depending on the case. In other settings, the case may not be important to the semantic interpretation of the word because it is caused by grammar-specific constraints like the beginning of a sentence. Therefore, language-specific heuristics are required in order to make decisions on how the case is treated. Punctuation marks such as hyphens need to be parsed carefully in order to ensure proper tokenization.

## 计算机代写|机器学习代写machine learning代考|Dimensionality Reduction and Matrix Factorization

Dimensionality reduction and matrix factorization fall in the general category of methods that are also referred to as latent factor models. Sparse and high-dimensional representations like text work well with some learning methods but not with others. Therefore, a natural question arises as whether one can somehow compress the data representation to express it in a smaller number of features. Since these features are not observed in the original data but represent hidden properties of the data, they are also referred to as latent features.
Dimensionality reduction is intimately related to matrix factorization. Most types of dimensionality reduction transform the data matrices into factorized form. In other words, the original data matrix $D$ can be approximately represented as a product of two or more matrices, so that the total number of entries in the factorized matrices is far fewer than the number of entries in the original data matrix. A common way of representing an $n \times d$ document-term matrix as the product of an $n \times k$ matrix $U$ and a $d \times k$ matrix $V$ is as follows:
$$D \approx U V^T$$
The value of $k$ is typically much smaller than $n$ and $d$. The total number of entries in $D$ is $n \cdot d$, whereas the total number of entries in $U$ and $V$ is only $(n+d) \cdot k$. For small values of $k$, the representation of $D$ in terms of $U$ and $V$ is much more compact. The $n \times k$ matrix $U$ contains the $k$-dimensional reduced representation of each document in its rows, and the $d \times k$ matrix $V$ contains the $k$ basis vectors in its columns. In other words, matrix factorization methods create reduced representations of the data with (approximate) linear transforms. Note that Equation $1.2$ is represented as an approximate equality. In fact, all forms of dimensionality reduction and matrix factorization are expressed as optimization models in which the error of this approximation is minimized. Therefore, dimensionality reduction effectively compresses the large number of entries in a data matrix into a smaller number of entries with the lowest possible error.

Popular methods for dimensionality reduction in text include latent semantic analysis, non-negative matrix factorization, probabilistic latent semantic analysis, and latent Dirichlet allocation. We will address most of these methods for dimensionality reduction and matrix factorization in Chapter 3 . Latent semantic analysis is the text-centric avatar of singular value decomposition.

Dimensionality reduction and matrix factorization are extremely important because they are intimately connected to the representational issues associated with text data. In data mining and machine learning applications, the representation of the data is the key in designing an effective learning method. In this sense, singular value decomposition methods enable high-quality retrieval, whereas certain types of non-negative matrix factorization methods enable high-quality clustering. In fact, clustering is an important application of dimensionality reduction, and some of its probabilistic variants are also referred to as topic models. Similarly, certain types of decision trees for classification show better performance with reduced representations. Furthermore, one can use dimensionality reduction and matrix factorization to convert a heterogeneous combination of text and another data type into multidimensional format (cf. Chapter 8).

# 机器学习代考

## 计算机代写|机器学习代写machine learning代考|Text Preprocessing and Similarity Computation

1. 文本提取：在文本源是 Web 的情况下，它会与各种其他类型的数据（例如锚点、标签等）结合使用。此外，在以 Web 为中心的设置中，特定页面可能包含（有用的）主要块和包含广告或不相关内容的其他块。从主块中提取有用的文本对于高质量挖掘很重要。这些类型的设置需要专门的解析和提取技术。
2. 停用词去除：停用词是经常出现的词，对挖掘过程几乎没有辨别力。常用代词、冠词和介词被视为停用词。需要删除此类词以改进挖掘过程。
3. 词干提取、大小写折叠和标点符号：具有共同词根的单词被合并为一个代表。例如，“sinking”和“sank”之类的词被合并为单个标记“sink”。单词第一个字母的大小写（即大写）对其语义解释可能重要也可能不重要。例如，“Rose”这个词可能是一朵花，也可能是一个人的名字，视情况而定。在其他情况下，大小写对于单词的语义解释可能并不重要，因为它是由特定于语法的约束（例如句子的开头）引起的。因此，需要特定语言的启发式方法来决定如何处理案例。需要仔细解析连字符等标点符号，以确保正确的标记化。

## 有限元方法代写

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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。