机器学习

学习过程的基本组成部分

无论是人类还是机器，学习过程都可以分为四个部分：数据存储、抽象、泛化和评估。图1.1展示了学习过程中的各个组成部分和所涉及的步骤。

1. 数据存储

存储和检索大量数据的能力是学习过程的重要组成部分。人类和计算机都利用数据存储作为高级推理的基础。

• 对于人类来说，数据存储在大脑中，并通过电化学信号进行检索。
• 计算机则使用硬盘驱动器、闪存、随机存取存储器和类似设备来存储数据，并利用电缆及其他技术来检索数据。

2. 抽象

学习过程的第二个组成部分是抽象。

抽象是提取关于存储数据知识的过程。这涉及创建关于整个数据的通用概念。知识的创建包括应用已知模型和创建新模型。

将模型拟合到数据集的过程称为训练。当模型经过训练后，数据就会转化为一种抽象形式，概括了原始信息。

3. 泛化

学习过程的第三个组成部分是泛化。

泛化描述了将关于存储数据的知识转化为可用于未来行动的形式的过程。这些行动将在与之前所见任务相似但不完全相同的任务上执行。在泛化中，目标是发现数据中与未来任务最相关的属性。

4. 评估

评估是学习过程的最后一个组成部分。

它是向用户提供反馈以衡量所学知识效用的过程。然后，该反馈用于改进整个学习过程。

机器学习的应用

将机器学习方法应用于大型数据库被称为数据挖掘。在数据挖掘中，会处理大量数据以构建一个具有有价值用途的简单模型，例如，具有高预测准确性的模型。

以下是机器学习的一些典型应用列表：

1. 在零售业务中，机器学习用于研究消费者行为。
2. 在金融领域，银行分析其历史数据以构建模型，用于信用申请、欺诈检测和股票市场。
3. 在制造业中，学习模型用于优化、控制和故障排除。
4. 在医学领域，学习程序用于医疗诊断。
5. 在电信领域，分析呼叫模式以进行网络优化和最大化服务质量。
6. 在科学领域，物理、天文学和生物学中的大量数据只能通过计算机进行足够快的分析。万维网庞大且不断增长，手动搜索相关信息是不可能完成的。
7. 在人工智能中，它用于教系统学习和适应变化，这样系统设计者就不必预见并为所有可能情况提供解决方案。
8. 它用于解决视觉、语音识别和机器人领域的许多问题。
9. 机器学习方法应用于计算机控制车辆的设计，以便在各种道路上正确转向。
10. 机器学习方法已被用于开发下棋、西洋双陆棋和围棋等游戏程序。

Basic components of learning process
The learning process, whether by a human or a machine, can be divided into four components,
namely, data storage, abstraction, generalization and evaluation. Figure 1.1 illustrates the
variouscomponents and the steps involved in the learning process.

1. Data storage
Facilities for storing and retrieving huge amounts of data are an important component of the
learning process. Humans and computers alike utilize data storage as a foundation for advanced
reasoning.
• In a human being, the data is stored in the brain and data is retrieved using electrochemical signals.
• Computers use hard disk drives, flash memory, random access memory and similar devices to store
data and use cables and other technology to retrieve data.
2. Abstraction
The second component of the learning process is known as abstraction.
Abstraction is the process of extracting knowledge about stored data. This involves creating general
concepts about the data as a whole. The creation of knowledge involves application of known models
and creation of new models.
The process of fitting a model to a dataset is known as training. When the model has been trained, the
data is transformed into an abstract form that summarizes the original information.
3. Generalization
The third component of the learning process is known as generalisation.
The term generalization describes the process of turning the knowledge about stored data into a form
that can be utilized for future action. These actions are to be carried out on tasks that are similar, but
not identical, to those what have been seen before. In generalization, the goal is to discover those
properties of the data that will be most relevant to future tasks.
4. Evaluation
Evaluation is the last component of the learning process.
It is the process of giving feedback to the user to measure the utility of the learned knowledge. This
feedback is then utilised to effect improvements in the whole learning process
Applications of machine learning
Application of machine learning methods to large databases is called data mining. In data
mining, a large volume of data is processed to construct a simple model with valuable use, for example,
having
high predictive accuracy.
The following is a list of some of the typical applications of machine learning.
1. In retail business, machine learning is used to study consumer behaviour.
2. In finance, banks analyze their past data to build models to use in credit applications, fraud
detection, and the stock market.
3. In manufacturing, learning models are used for optimization, control, and troubleshooting.
4. In medicine, learning programs are used for medical diagnosis.
5. In telecommunications, call patterns are analyzed for network optimization and maximizing the
quality of service.
6. In science, large amounts of data in physics, astronomy, and biology can only be analyzed fast
enough by computers. The World Wide Web is huge; it is constantly growing and searching for
relevant information cannot be done manually.
7. In artificial intelligence, it is used to teach a system to learn and adapt to changes so that the
system designer need not foresee and provide solutions for all possible situations.
8. It is used to find solutions to many problems in vision, speech recognition, and robotics.
9. Machine learning methods are applied in the design of computer-controlled vehicles to steer
correctly when driving on a variety of roads.
10. Machine learning methods have been used to develop programmes for playing games such as
chess, backgammon and Go.