模式识别与机器学习 Pattern Recognition and Machine Learning 学习总结

本文是基于KTH课程《Pattern Recognition and Machine Learning》的学习总结,涵盖了决策函数、GMM、贝叶斯分类、HMM等多个核心概念,并探讨了实际应用中的分类问题和EM算法等。

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这篇文章是我复习KTH课程Pattern Recognition and Machine Learning时的学习笔记,主要的参考资料为该课程课本。

有可能会出现图片打不开的情况,翻墙会解决这个问题

目录

Chapter 1

△Decision&Discriminant Function

△GMM

Chapter 3 Bayesian Pattern Classification

△MAP

△ML

△ML,MAP和最小风险法则

△具体步骤

△fundamental Bayes Rule

△本章总结

Chapter 4 Classification in Practical Applications

△practical problems

△稀疏化模型

△Some Important Concepts in Applied Classification

Chapter 5 HMM

△Three key factors make the HMM very simple to apply

△各类马尔科夫过程

△Forward & Backward Algorithm

△Vertibi algorithm

Chapter 7 EM Algorithm

Chapter 8 Bayesian Learning


Chapter 1

△Decision&Discriminant Function

d(x): decision function;  g(x):discriminant function (书P15)

(给出了threshold)

△GMM

 

Chapter 3 Bayesian Pattern Classification

△MAP

△ML

△ML,MAP和最小风险法则

△具体步骤

△fundamental Bayes Rule

△本章总结

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications., This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory., The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.
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