模式识别与机器学习
看了好几篇讲模式识别与机器学习区别的文章，都各有所见，这里翻译分享一篇我觉得最有道理的外文文章。
Introduction
In very simple language, Pattern Recognition is a type of problem while Machine Learning is a type of solution. Pattern recognition is closely related to Artificial Intelligence and Machine Learning. Pattern Recognition is an engineering application of Machine Learning. Machine Learning deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions whereas Pattern recognition is the recognition of patterns and regularities in data.
简单来说，模式识别是一类问题而机器学习是一种解决问题的方法。模式识别与人工智能和机器学习密切相关，它是机器学习在工程上的一种应用。机器学习是让机器构造和学习一个可以从数据中学习的系统，而不是让机器遵循显式编程的指令。而模式识别对数据中的模式(什么是模式)和规律的识别。
1 Machine Learning
The goal of Machine Learning is never to make “perfect” guesses because Machine Learning deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful.
Machine Learning is a method of data analysis that automates analytical model building. Machine Learning is a field that uses algorithms to learn from data and make predictions. A Machine Learning algorithm then takes these examples and produces a program that does the job. Machine Learning builds heavily on statistics. For example, when we train our machine to learn, we have to give it a statistically significant random sample as training data. If the training set is not random, we run the risk of the Machine Learning patterns that aren’t actually there.
机器学习的目标绝不是做出“完美”的猜测，而是找到一个对解决实际问题有效的估计，因为机器学习所涉及的领域是还不能被（数学）完美解释的。
机器学习是一种自动建立分析模型的数据分析方法。机器学习是一个使用算法从数据中学习从而具有预测功能的领域。算法可以通过一些实例学习并产生一个具有预测功能的系统。机器学习在很大程度上建立在统计学基础上。例如，当我们训练机器学习时，我们必须给它一个统计上显著的随机样本作为训练数据。如果训练集不是随机的，那么机器学习学习到的模式可能是错误的。
2 Pattern Recognition
Pattern recognition is the process of recognizing patterns by using a Machine Learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation.
In IT, pattern recognition is a branch of Machine Learning that emphasizes the recognition of data patterns or data regularities in a given scenario. Pattern recognition involves classification and cluster of patterns.
模式识别是使用机器学习算法识别模式的过程。模式识别可以定义为基于已经获得的知识或从模式(或模式的表征信息)中提取的统计信息，利用机器学习算法来数据进行分类。
在IT领域，模式识别是机器学习的一个分支，它强调对给定场景中的数据模式或数据规律的识别。模式识别涉及到模式的分类和聚类。
3 Features of Pattern Recognition:
- Pattern recognition completely rely on data and derives any outcome or model from data itself
- Pattern recognition system should recognise familiar pattern quickly and accurate
- Recognize and classify unfamiliar objects very quickly
- Accurately recognize shapes and objects from different angles
- Identify patterns and objects even when partly hidden
- Recognise patterns quickly with ease, and with automaticity
- Pattern recognition always learn from data
- 模式识别完全依赖于数据，并从数据本身获得结果或模型
- 模式识别系统需要快速准确地识别出熟悉的模式
- 快速识别和分类不熟悉的物体
- 从不同角度准确识别形状和物体
- 即使部分隐藏，也要识别图案和对象
- 快速、轻松、自动地识别模式
- 模式识别总是从数据中学习
4 Difference Between Machine Learning and Pattern Recognition
Differences Between Machine Learning and Pattern Recognition:
| 机器学习 | 模式识别 |
|---|---|
| 机器学习是一种自动建立分析模型的数据分析方法。 | 模式识别是各种算法的工程应用，目的是识别数据中的模式 |
| 机器学习偏实际 | 模式识别偏理论 |
| 能解决实时的问题 | 可以是实时的问题 |
| 我们需要机器或计算机来应用机器学习算法 | 模式识别可能不是机器完成的（比如人也可以） |
英文与图像来源：https://dzone.com/articles/machine-learning-and-pattern-recognition
THE END.
感谢阅读。
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