### 计算机代写|机器学习代写machine learning代考|COMP4702

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 Clustering

Text clustering methods partition the corpus into groups of related documents belonging to particular topics or categories. However, these categories are not known a priori, because specific examples of desired categories (e.g., politics) of documents are not provided up front. Such learning problems are also referred to as unsupervised, because no guidance is provided to the learning problem. In supervised applications, one might provide examples of news articles belonging to several natural categories like sports, politics, and so on. In the unsupervised setting, the documents are partitioned into similar groups, which is sometimes achieved with a domain-specific similarity function like the cosine measure. In most cases, an optimization model can be formulated, so that some direct or indirect measure of similarity within a cluster is maximized. A detailed discussion of clustering methods is provided in Chapter 4.

Many matrix factorization methods like probabilistic latent semantic analysis and latent Dirichlet allocation also achieve a similar goal of assigning documents to topics, albeit in a soft and probabilistic way. A soft assignment refers to the fact that the probability of assignment of each document to a cluster is determined rather than a hard partitioning of the data into clusters. Such methods not only assign documents to topics but also infer the significance of the words to various topics. In the following, we provide a brief overview of various clustering methods.

Most forms of non-negative matrix factorization methods can be used for clustering text data. Therefore, certain types of matrix factorization methods play the dual role of clustering and dimensionality reduction, although this is not true across every matrix factorization method. Many forms of non-negative matrix factorization are probabilistic mixture models, in which the entries of the document-term matrix are assumed to be generated by a probabilistic process. The parameters of this random process can then be estimated in order to create a factorization of the data, which has a natural probabilistic interpretation. This type of model is also referred to as a generative model because it assumes that the document-term matrix is created by a hidden generative process, and the data are used to estimate the parameters of this process.

## 计算机代写|机器学习代写machine learning代考|Similarity-Based Algorithms

Similarity-based algorithms are typically either representative-based methods or hierarchical methods, In all these cases, a distance or similarity function between points is used to partition them into clusters in a deterministic way. Representative-based algorithms use representatives in combination with similarity functions in order to perform the clustering. The basic idea is that each cluster is represented by a multi-dimensional vector, which represents the “typical” frequency of words in that cluster. For example, the centroid of a set of documents can be used as its representative. Similarly, clusters can be created by assigning documents to their closest representatives such as the cosine similarity. Such algorithms often use iterative techniques in which the cluster representatives are extracted as central points of clusters, whereas the clusters are created from these representatives by using cosine similarity-based assignment. This two-step process is repeated to convergence, and the corresponding algorithm is also referred to as the $k$-means algorithm. There are many variations of representative-based algorithms although only a small subset of them work with the sparse and high-dimensional representation of text. Nevertheless, one can use a broader variety of methods if one is willing to transform the text data to a reduced representation with dimensionality reduction techniques.

In hierarchical clustering algorithms, similar pairs of clusters are aggregated into larger clusters using an iterative approach. The approach starts by assigning each document to its own cluster and then merges the closest pair of clusters together. There are many variations in terms of how the pairwise similarity between clusters is computed, which has a direct impact on the type of clusters discovered by the algorithm. In many cases, hierarchical clustering algorithms can be combined with representative clustering methods to create more robust methods.

# 机器学习代考

## 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。