Pattern Classification Learning Planning

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The opening crap.

Learning Image Processing series has been a long time, the basic image processing algorithm also have a certain understanding, because want to further study computer vision direction, so pattern classification is inevitable to learn knowledge, this series of blog will not include machine learning class algorithm, machine learning algorithm will be in another series detailed introduction.
The differences between pattern recognition and pattern classification are briefly understood, and the main tasks of pattern recognition include extracting feature points and classifier design. Pattern classification is mainly the knowledge of designing classifiers.
The extraction of feature points will also be introduced in another series of blogs.
Learning Image Processing Series follow-up will be updated some basic common knowledge, such as repair and compression will no longer be introduced in detail, the focus of research direction in the recognition algorithm and application.

Study Plan

The main line of image processing is-Gonzalez's digital image processing
The main textbook for the study of pattern classification here is "pattern Classification", Richard, etc.

Pattern recognition algorithm Independence is strong, unlike image processing has a strong knowledge coherence, the following blog will follow each algorithm a blog of the way introduced.
Some of the chapters that you might want to learn include:

Chapters	content	algorithm
1	Introduction
2	Bayesian decision-making theory
3	Maximum likelihood estimation and Bayesian parameter estimation
4	Non-parametric technology
5	Linear discriminant function
7	Random method
8	Non-metric methods

Blog update may be a bit long period, after all, is to learn new knowledge, and math requirements are more, you are welcome to teach a lot.

Pattern Classification Learning Plan