### 统计代写|机器学习作业代写machine learning代考| Many Roads to Concept Learning

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

## 统计代写|机器学习作业代写machine learning代考|Facing the Real World

The reader now understands that learning from pre-classified training examples is not easy. So many obstacles stand in the way. Even if the training set is perfect and noise-free, many classifiers can be found that are capable of correctly classifying all training examples but will differ in their treatment of examples that were not seen during learning. How to choose the best one?

Facing the Real World The training examples are rarely perfect. Most of the time, the class labels and attributes are noisy, a lot of the available information is irrelevant, redundant, or missing, the training set may be far too small to capture all critical aspects – the list goes on and on. There is no simple solution. No wonder that an entire scientific discipline-machine learning-has come to being that seeks to

come to grips with all the above-mentioned issues and to illuminate all the tangled complications of the underlying tasks.

As pointed out by Fig. 1.4, engineers have at their disposal several major and some smaller paradigms, each marked by different properties, each exhibiting different strengths and shortcomings when applied to a concrete task. To show the nature of each of these frameworks, and to explain how it behaves under diverse circumstances is the topic for the rest of this book. But perhaps we can mention here at least some of the basic principles.

## 统计代写|机器学习作业代写machine learning代考|Other Ambitions of Machine Learning

Induction of classifiers is the most popular machine-learning task-but not the only one! Let us briefly survey some of the other topics covered in this book.

Unsupervised Learning A lot of information can be gleaned even from examples that are not labeled with classes. To begin with, analysis can reveal that the examples create clusters of similar attribute vectors. Each such cluster can exhibit different properties that may deserve to be studied.

We also know how to map unlabeled $N$-dimensional vectors to a neural field. The resulting two-dimensional matrix helps visualize the data in ways different from classical cluster analysis. One can see which parts of the instance space are densely populated and which parts sparsely, we may even learn how many exceptions there are. Approaches based on the so-called auto-encoding can create from existing attributes meaningful higher-level attributes; such re-description often facilitates learning in domains marked by excessive detail.

Reinforcement Learning Among the major triumphs of machine learning, perhaps the most fascinating are computers beating the best humans in such games as chess, Backgammon, and Go. For generations, such feats were deemed impossible! And yet, here we are. Computer programs can learn to become proficient simply by playing innumerable games against themselves-and by learning from this experience. What other proof of the potential of our discipline does anybody want?
The secret behind these accomplishments is the techniques known as reinforcement learning, frequently in combination with artificial neural networks and deep learning. The application field is much broader than just game playing. The idea is to let the machine develop an ability to act in real-world environments, to react to changes in this environment, to optimize its behavior in tasks ranging from polebalancing to vehicle navigation to advanced decision-making in domains that lack detailed technical description.

## 统计代写|机器学习作业代写machine learning代考|Summary and Historical Remarks

Induction from a training set of pre-classified examples is the most deeply studied machine-learning task.
Historically, the task is cast as search. This, however, is not enough. The book explores a whole range of more useful techniques.
Classification performance is estimated with the help of pre-classified testing data. The simplest performance criterion is error rate, the percentage of examples misclassified by the classifier.
Two classifiers that both correctly classify all training examples may differ significantly in their handling of future examples.
Apart from low error rate, some applications require that the classifier provides the reasons behind the classification.
The quality of the induced classifier depends on training examples. The quality of the training examples depends not only on their choice but also on the attributes used to describe them. Some attributes are relevant, others irrelevant or redundant. Quite often, critical attributes are missing.
The attribute values and class labels may suffer from stochastic noise, systematic noise, and random artefacts. The value of an attribute in a concrete example may not be known.

## 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。