Recently, I am studying "Pattern Recognition" (Tsinghua University. Zhang Xuegong. Third Edition) due to work needs. I probably flipped through the whole book and found that many of the knowledge backgrounds mentioned in artificial intelligence are actually taught in the same way. This article will focus on this book and The relationship with artificial intelligence is briefly introduced.
The idea of the book "Pattern Recognition" is the general idea of the total score. It first introduces various pattern recognition (artificial intelligence) methods through the previous chapters, and then summarizes and introduces in two chapters how to use pattern recognition methods (determine features, propose features) , How to evaluate the application of pattern recognition methods (error rate estimation, efficiency estimation). Different from the mainstream machine learning and artificial intelligence books, the main content of this book is to explain the learning method based on statistical data, that is, statistical machine learning. The main content of this book is as follows:
The first chapter, the introduction part, introduces data-based pattern recognition through the big background and big concepts;
The second chapter, statistical decision-making methods (mainly Bayesian decision-making methods), introduces important concepts related to pattern recognition such as minimum error rate Bayesian decision-making, minimum risk Bayesian decision-making, and ROC;
Chapter 3, Probability Estimation Methods, introduces the similarities and differences between probability estimation methods and Bayesian estimation methods, as well as non-parametric estimation methods;
Chapter 4, Linear Classifier, introduces the basic concepts of linear classifier, common linear classifiers such as Fisher classifier, perceptron, SVM, and multi-class linear classifier;
Chapter 5, Non-Linear Classifiers, based on Chapter 4, introduces common non-linear classification methods in the industry such as NN, SVM, and kernel functions;
Chapter 6 introduces the nearest neighbor method, DT, random forest, logistic regression, and boosting methods.
The seventh and eighth chapters introduce the selection of classification features and the method of classification feature extraction from the perspective of method application, and supplement the application of the method.
From the second chapter to the eighth chapter, it mainly focuses on the method level of supervised recognition (that is, supervised learning)
Chapter 9 focuses on some unsupervised recognition (unsupervised learning) methods, such as multiple clustering methods, self-organizing mapping neural networks, etc.
Finally, Chapter 10 gives the judgment of the application performance of the pattern recognition method to help learners determine whether the models and methods they build are appropriate and reasonable.
Looking back, artificial intelligence is undoubtedly the hottest technology at present. There is no one. The popularity of various industries has been successfully switched from cutting-edge concepts such as Internet +, cloud computing, and big data to artificial intelligence. Although artificial intelligence is very popular, artificial intelligence is not essentially a brand new thing. As early as the 1950s, when Turing published the Turing machine, it actually represented the prototype of artificial intelligence. After decades of development, artificial intelligence has experienced ups and downs, and finally reached today's popularity, which is also an inevitable law of historical development.
Artificial intelligence may be a brand-new concept for many people, but it is not unfamiliar to science and engineering students. I don’t know if you still remember many courses that you have encountered at the undergraduate and graduate levels, such as "Statistics Theory" and "Linear Algebra" "System Identification", "Pattern Recognition", "Neural Networks" and so on. These courses all include the basic knowledge of artificial intelligence. After the previous introduction, we can see that the essence of pattern recognition is a branch of artificial intelligence based on statistical methods, which belongs to the included relationship. Therefore, you will not be embarrassed by professional terms.
Generally speaking, the book is rich in content and wide in coverage. This also means that the book does not have too much space to discuss each type of identification method. In most cases, this book focuses on carefully deriving the principles and framework of each type of method. , The outline and guidance build a knowledge framework in the field of pattern recognition. This book and "Statistical Learning Methods" (Li Hang, Beijing University of Aeronautics and Astronautics) have some similarities, students who are interested in this aspect can read it together.
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Reply: Pattern recognition, get the "Pattern Recognition" e-book.
Reply: Statistical Learning Methods, get the "Statistical Learning Methods" e-book.