How to distinguish between machine learning and pattern recognition?

With the rise of artificial intelligence in recent years , machine learning 、 Pattern recognition has also become a hot word , Appear frequently in front of the public . Although it is often said that machine learning and pattern recognition , But few people can clearly distinguish between the two . This article will take you to fully understand the concepts of machine learning and pattern recognition 、 Difference and connection .

How to distinguish between machine learning and pattern recognition ？

One 、 Concept

1、 machine learning

machine learning (Machine Learning, ML) It's an interdisciplinary subject , Probability theory 、 statistical 、 Approximation theory 、 Convex analysis 、 Algorithm complexity theory and other disciplines . To study how computers simulate or implement human learning behavior , To acquire new knowledge or skills , Reorganize the existing knowledge structure to improve its performance . It's the core of AI , It's the fundamental way to make computers intelligent , Its application covers all fields of artificial intelligence , It mainly uses induction 、 Synthesize, not deduce .

A machine based on a large number of samples of something , Sum up the general laws of this type of things , The skills used in the summary process are what we often call algorithms . When enough samples enable the algorithm to summarize a set of effective laws , Machines can use these laws to make decisions and predict events in the real world .

2、 pattern recognition

Pattern recognition rose in the 1950s , It was all the rage in the 1970s and 1980s , It is an important part of information science and artificial intelligence , It is mainly used in image analysis and processing 、 speech recognition 、 Sound classification 、 signal communication 、 Computer aided diagnosis 、 Data mining, etc . Pattern recognition system process ： Feature extraction and selection ; Train to learn ; classification . For example, after a human sees something , They are usually classified subconsciously ： Animals or plants , Which family does it belong to , Whether it can be used for medicinal purposes , Is there any fruit , Whether the flowers are beautiful , Is it poisonous …… This large list of categories constitutes people's overall cognition of this kind of thing . This belongs to human pattern recognition , This skill is very important for people and even some animals , It is very simple and almost inborn .

Two 、 difference

1、 The development trend is different

Historically speaking , These twin brothers are both the schools with a brilliant time in the history of artificial intelligence . Among them, pattern recognition can be classified as a veteran in the field of artificial intelligence . Although pattern recognition looks very tall , And it has been used for a long time , But the effect always seems to be unsatisfactory . There seems to be some signs of passing away , Is slowly declining and disappearing . Machine learning is the most basic and popular player in the field of artificial intelligence .

2、 Different application ranges

At present, machine learning is moving faster in the narrow field of artificial intelligence , But the breadth is still the pattern . Pattern recognition in many classical fields , Such as signal processing , Computer image and computer vision , Natural language analysis has been developing continuously .

3、 Different judgment points

Machine learning trains models based on samples , For example, the trained neural network is a model for specific classification problems ; The point is “ Study ”, The process of training the model is learning ; The end result of machine learning is thinking . Machine learning focuses on when the characteristics are not clear , Using some universal algorithm to give classification rules . The concept of machine learning can be analogous to cluster analysis ( Clustering itself is a typical machine learning method ), Yes “ class ” The strict definition of is not clear , Not to mention testing .

Pattern recognition is based on existing features , The parameters in the model are given by parametric or nonparametric methods , So as to achieve the purpose of discrimination . Those who have studied multivariate statistics can understand it this way ： The concept of pattern recognition can be compared with discriminant analysis , Is to determine the , Testable , Having a statistical background ( Or further, the basic theoretical background of organic rationality ).

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