《贝叶斯学习与随机矩阵及在无线通信中的应用 BI-RM-AWC》课程教学资源（文献书籍）Pattern Recognition and Machine Learning

Christopher M.Bishop Pattern Recognition and Machine Learning ②Springer

Christopher M. Bishop Pattern Recognition and Machine Learning

This book is dedicated to my family: Jenna,Mark,and Hugh Total eclipse of the sun,Antalya,Turkey,29 March 2006

This book is dedicated to my family: Jenna, Mark, and Hugh Total eclipse of the sun, Antalya, Turkey, 29 March 2006

Preface Pattern recognition has its origins in engineering,whereas machine learning grew out of computer science.However,these activities can be viewed as two facets of the same field,and together they have undergone substantial development over the past ten years.In particular,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 models.Also,the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation.Similarly,new models based on kernels have had significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a compre- hensive 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,and assumes no previous knowledge of pattern recognition or ma- chine learning concepts.Knowledge of multivariate calculus and basic linear algebra is required,and some familiarity with probabilities would be helpful though not es- sential as the book includes a self-contained introduction to basic probability theory. Because this book has broad scope,it is impossible to provide a complete list of references,and in particular no attempt has been made to provide accurate historical attribution of ideas.Instead,the aim has been to give references that offer greater detail than is possible here and that hopefully provide entry points into what,in some cases,is a very extensive literature.For this reason,the references are often to more recent textbooks and review articles rather than to original sources. The book is supported by a great deal of additional material,including lecture slides as well as the complete set of figures used in the book,and the reader is encouraged to visit the book web site for the latest information: http://research.microsoft.com/~cmbishop/PRML vii

Preface Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, 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 models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa￾gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a compre￾hensive 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, and assumes no previous knowledge of pattern recognition or ma￾chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not es￾sential as the book includes a self-contained introduction to basic probability theory. Because this book has broad scope, it is impossible to provide a complete list of references, and in particular no attempt has been made to provide accurate historical attribution of ideas. Instead, the aim has been to give references that offer greater detail than is possible here and that hopefully provide entry points into what, in some cases, is a very extensive literature. For this reason, the references are often to more recent textbooks and review articles rather than to original sources. The book is supported by a great deal of additional material, including lecture slides as well as the complete set of figures used in the book, and the reader is encouraged to visit the book web site for the latest information: http://research.microsoft.com/∼cmbishop/PRML vii

viii PREFACE Exercises The exercises that appear at the end of every chapter form an important com- ponent of the book.Each exercise has been carefully chosen to reinforce concepts explained in the text or to develop and generalize them in significant ways,and each is graded according to difficulty ranging from ()which denotes a simple exercise taking a few minutes to complete,through to (**)which denotes a significantly more complex exercise. It has been difficult to know to what extent these solutions should be made widely available.Those engaged in self study will find worked solutions very ben- eficial,whereas many course tutors request that solutions be available only via the publisher so that the exercises may be used in class.In order to try to meet these conflicting requirements,those exercises that help amplify key points in the text,or that fill in important details,have solutions that are available as a PDF file from the book web site.Such exercises are denoted byww.Solutions for the remaining exercises are available to course tutors by contacting the publisher(contact details are given on the book web site).Readers are strongly encouraged to work through the exercises unaided,and to turn to the solutions only as required. Although this book focuses on concepts and principles,in a taught course the students should ideally have the opportunity to experiment with some of the key algorithms using appropriate data sets.A companion volume (Bishop and Nabney, 2008)will deal with practical aspects of pattern recognition and machine learning, and will be accompanied by Matlab software implementing most of the algorithms discussed in this book. Acknowledgements First of all I would like to express my sincere thanks to Markus Svensen who has provided immense help with preparation of figures and with the typesetting of the book in IATEX.His assistance has been invaluable. I am very grateful to Microsoft Research for providing a highly stimulating re- search environment and for giving me the freedom to write this book (the views and opinions expressed in this book,however,are my own and are therefore not neces- sarily the same as those of Microsoft or its affiliates). Springer has provided excellent support throughout the final stages of prepara- tion of this book,and I would like to thank my commissioning editor John Kimmel for his support and professionalism,as well as Joseph Piliero for his help in design- ing the cover and the text format and MaryAnn Brickner for her numerous contribu- tions during the production phase.The inspiration for the cover design came from a discussion with Antonio Criminisi. I also wish to thank Oxford University Press for permission to reproduce ex- cerpts from an earlier textbook,Neural Networks for Pattern Recognition (Bishop, 1995a).The images of the Mark 1 perceptron and of Frank Rosenblatt are repro- duced with the permission of Arvin Calspan Advanced Technology Center.I would also like to thank Asela Gunawardana for plotting the spectrogram in Figure 13.1, and Bernhard Scholkopf for permission to use his kernel PCA code to plot Fig- ure12.17

viii PREFACE Exercises The exercises that appear at the end of every chapter form an important com￾ponent of the book. Each exercise has been carefully chosen to reinforce concepts explained in the text or to develop and generalize them in significant ways, and each is graded according to difficulty ranging from (⋆), which denotes a simple exercise taking a few minutes to complete, through to (⋆⋆⋆), which denotes a significantly more complex exercise. It has been difficult to know to what extent these solutions should be made widely available. Those engaged in self study will find worked solutions very ben￾eficial, whereas many course tutors request that solutions be available only via the publisher so that the exercises may be used in class. In order to try to meet these conflicting requirements, those exercises that help amplify key points in the text, or that fill in important details, have solutions that are available as a PDF file from the book web site. Such exercises are denoted by www . Solutions for the remaining exercises are available to course tutors by contacting the publisher (contact details are given on the book web site). Readers are strongly encouraged to work through the exercises unaided, and to turn to the solutions only as required. Although this book focuses on concepts and principles, in a taught course the students should ideally have the opportunity to experiment with some of the key algorithms using appropriate data sets. A companion volume (Bishop and Nabney, 2008) will deal with practical aspects of pattern recognition and machine learning, and will be accompanied by Matlab software implementing most of the algorithms discussed in this book. Acknowledgements First of all I would like to express my sincere thanks to Markus Svensen who ´ has provided immense help with preparation of figures and with the typesetting of the book in LATEX. His assistance has been invaluable. I am very grateful to Microsoft Research for providing a highly stimulating re￾search environment and for giving me the freedom to write this book (the views and opinions expressed in this book, however, are my own and are therefore not neces￾sarily the same as those of Microsoft or its affiliates). Springer has provided excellent support throughout the final stages of prepara￾tion of this book, and I would like to thank my commissioning editor John Kimmel for his support and professionalism, as well as Joseph Piliero for his help in design￾ing the cover and the text format and MaryAnn Brickner for her numerous contribu￾tions during the production phase. The inspiration for the cover design came from a discussion with Antonio Criminisi. I also wish to thank Oxford University Press for permission to reproduce ex￾cerpts from an earlier textbook, Neural Networks for Pattern Recognition (Bishop, 1995a). The images of the Mark 1 perceptron and of Frank Rosenblatt are repro￾duced with the permission of Arvin Calspan Advanced Technology Center. I would also like to thank Asela Gunawardana for plotting the spectrogram in Figure 13.1, and Bernhard Scholkopf for permission to use his kernel PCA code to plot Fig- ¨ ure 12.17

PREFACE ix Many people have helped by proofreading draft material and providing com- ments and suggestions,including Shivani Agarwal,Cedric Archambeau,Arik Azran, Andrew Blake,Hakan Cevikalp,Michael Fourman,Brendan Frey,Zoubin Ghahra- mani,Thore Graepel,Katherine Heller,Ralf Herbrich,Geoffrey Hinton,Adam Jo- hansen,Matthew Johnson,Michael Jordan,Eva Kalyvianaki,Anitha Kannan,Julia Lasserre,David Liu,Tom Minka,Ian Nabney,Tonatiuh Pena,Yuan Qi,Sam Roweis, Balaji Sanjiya,Toby Sharp,Ana Costa e Silva,David Spiegelhalter,Jay Stokes,Tara Symeonides,Martin Szummer,Marshall Tappen,Ilkay Ulusoy,Chris Williams,John Winn,and Andrew Zisserman. Finally,I would like to thank my wife Jenna who has been hugely supportive throughout the several years it has taken to write this book. Chris Bishop Cambridge February 2006

PREFACE ix Many people have helped by proofreading draft material and providing com￾ments and suggestions, including Shivani Agarwal, Cedric Archambeau, Arik Azran, ´ Andrew Blake, Hakan Cevikalp, Michael Fourman, Brendan Frey, Zoubin Ghahra￾mani, Thore Graepel, Katherine Heller, Ralf Herbrich, Geoffrey Hinton, Adam Jo￾hansen, Matthew Johnson, Michael Jordan, Eva Kalyvianaki, Anitha Kannan, Julia Lasserre, David Liu, Tom Minka, Ian Nabney, Tonatiuh Pena, Yuan Qi, Sam Roweis, Balaji Sanjiya, Toby Sharp, Ana Costa e Silva, David Spiegelhalter, Jay Stokes, Tara Symeonides, Martin Szummer, Marshall Tappen, Ilkay Ulusoy, Chris Williams, John Winn, and Andrew Zisserman. Finally, I would like to thank my wife Jenna who has been hugely supportive throughout the several years it has taken to write this book. Chris Bishop Cambridge February 2006