实例介绍
【实例简介】
《模式分类》duda第二版所有课后习题的解答，全英文，清晰版，
Preface In writing this Solution Manual I havc lcarncd a vcry important lesson. As a student, I thought that the best way to master a subject was to go to a superb university and study with an established expert. Later, I realized instead that the best way was to teach a course on the subject. Yet later, I was convinced that the best way was to write a detailed and extensive textbook. Now I know that all these years I have been wrong: in fact the best way to master a subject is to write the Solution manual In solving the problems for this Manual I have been forced to confront myriad technical details that might have tripped up the unsuspecting student. Students and teachers can thank me for simplifying or screening out problems that required pages of unenlightening calculations. Occasionally I had to go back to the text and dclctc the word“ easily” from problem references that read“ it can easily be shown( Proble….).” Throughout, I have tried to choose data or problem conditions that are particularly instructive. In solving these problems, I have found errors in early drafts of this text (as well as errors in books by other authors and even in classic refereed papers), and thus the accompanying text has been improved for the writing of this Manual I have tried to make the problem solutions self-contained and self-explanatory have gone to great lengths to ensure that the solutions are correct and clearly presented many have been reviewed by students in several classes. Surely there are errors and typos in this manuscript, but rather than editing and rechecking these solutions over months or even ycars, I thought it best to distribute the Manual, howevcr flawed as early as possible. I accept responsibility for these inevitable errors, and humbly ask anyone finding them to contact me directly.(Please, however, do not ask me to explain a solution or help you solve a problem! It should be a small matter to change the Manual for future printings, and you should contact the publisher to check that you have the most recent version. Notice, too, that this Manual contains a list of known typos and errata in the text which you might wish to photocopy and distribute to students I have tried to be thorough in order to help students, even to the occassional fault of verbosity. You will notice that several problems have the simple"explain your answer in words and"graph your results. These were added for students to gain intuition and a deeper understanding. Graphing per se is hardly an intellectual challenge, but if the student graphs functions, he or she will develop intuition and remember problem and its results better. Furthermore, when the student later sees graphs of data from dissertation or research work, the link to the homework problem and the material in the text will be more readily apparent. Note that due to the vaga.ries of automatic typesetting, figures may appear on pages after their reference in this Manual; be sure to consult the full solution to any problem I have also included worked examples and so sample final exams with solutions to cover materia l in text. I distribute a list of important equations(without descriptions) with the exam so students can focus understanding and using equations, rather than memorizing them. I also include on every final exam one problem verbatim from a homework. taken from the book. i find this motivates students to review carefull their homework assignments, and allows somewhat more difficult problems to be in- cluded. These will be updated and expanded; thus if you have exam questions you find particularly appropriate, alld would like to share theIm, please send a copy(witll solutions) to me It should be noted, too, that a set of overhead transparency masters of the figures om the text are available to faculty adopters. I have found these to be invaluable for lecturing, and I put a set on reserve in the library for students. The files can be accessed through a standard web browser or an ftp client program at the Wiley stm ftp area at ftp: //ftp. wiley. com/p ch_med/pattern/ or from a link on the Wiley Electrical Engineering software supplements page at http://www.wiley.com/products/subject/engineering/electrical/ software_supplem_elec_eng. html I have taught. from the text (in various st ages of completion) at the Universit of California at Berkeley(Extension Division) and in three Departments at Stan ford University: Electrical Engineering, Statistics and Computer Science. Numerous students and colleagues have made suggestions. Especially noteworthy in this re- gard are Sudeshna Adak, Jian An, Sung-Hyuk Cha, Koichi Ejiri, Rick Guadette John Heumann, Travis Kopp, Yaxin Liu, Yunqian Ma Sayan Mukhcrjcc, Hirobumi Nishida, Erhan Oztop, Steven Rogers, Charles Roosen, Sergio Bermejo Sanchez, God fried Toussaint, Namrata Vaswani, Mohammed Yousuf and Yu Zhong. Thanks too go to Dick Duda who gave several excellent suggestions I would greatly appreciate notices of any errors in this Manual or the text itself. I would be especially grateful for solutions to problems not yet solved. Please send any Buch information to me at the below address. I will incorporate them into subsequent leases of this manual This manual is for the use of educators and must not be distributed in bulk to students in any form. Short excerpts may be photocopied and distributed, but only in conjunction with the use of Pattern Classification(2nd ed.) I wish you all the best of luck in teaching and research Ricoh innovations. Inc David G. Stork 2882 Sand hill road suite 115 Menlo Park. CA 94025-7022 USA storkgrii. ricoh. com Contents Pref 1 Introduction 2 Bayesian decision theory Problem sol Computer Exercises 74 3 Maximum likelihood and Bayesian parameter estimation 77 Problern solutions Computer exercises 130 4 Nonparametric techniques 131 Problem solu 131 Computer Exercises 174 5 Linear discriminant functions 177 Problcm solutio 177 Computer Exercises 217 6 Multilayer neural networks 219 Problem solutio 219 Computer Exercises 254 7 Stochastic rmethods 255 Problem solut ic 25.5 Computer Exercises 276 8 Nonmetric methods 277 Problern solutions 277 Computer Exercises 4 9 Algorithm-independent machine learning 295 Problem solutions 295 Computer exercises 304 10 Unsupervised learning and clustering 305 Problcm solutions 305 Conputer Exercises ..,..355 Sample final exams and solutions 357 CONTENTS Worked examples 415 Errata and ammendations in the text 417 First and second printings .417 Fifth printing 443 Chapter 1 Introduction Problem solutions There are neither problems nor computer exercises in Chapter 1 CHAPTER 1 INTRODUCTION Chapter 2 Bayesian decision theory Problem solutions Section 2.1 1. Equation 7 in the text states P(error )=minPw1c), P(w2l c) (a) We assume, without loss of generality, that for a given particular we have P(w2l c)> P(wllc), and thus P(error r)=P(wllr). We have, Moreover, the normalization condition P(w1n)=1-P(w2 r). Together these imply P(w2 c)> 1/2 or 2P(w2x)>1 and 2P(w2 )P(w1lr)> P(w1 r)=P(errorr) This is true at every and hence the integrals obey 2P(w2r)P(wila)da Plerrorl)d In short, 2P(w2c)P(wila) provides an upper bound for P(errorr (b) From part(a), wc havc that P(w2 ac)>1/ 2, but in th currcnt conditions not greater than 1/a for a 2. Take as an example, a=4 3 and P(w1a)=0.4 and hence P(w2)=0.6. In this case, P(errorr)=0.4. Moreover, we have aP(x1|-)P(u2x)-4/3×0.6×0.4<P( erTor|x) This does not provide an upper bound for all values of P(w1lac (c)Let P(error c)=P(w1l ). In that case, for all we have P(w2r)P(wia)< P(wia)p(error a 2f(1 vie)p(errorc)d.c and we have a lower bound CHAPTER 2. BAYESIAN DECISION THEORY (d) The solution to part(b) also applies here Section 2.2 2. Wc arc given that the dcnsity is of the form p(alwi) (a) We seek k so that the function is normalized, as required by a true density. We integrate this function, set, it to 1.0 k exp[(a-ai)/bi]dr+ expl(a-ai)/b, ]dx[=1 which yields 20; h=1 ork= 1/(20i). Note that the 1lormlalization is independent of ai, which corresponds to a shift, along t he axis and is hence indeed irrelevant. to normalization The distribution is therefore written (b The likelihood ratio can be written directly p(x|1)b2 p(x|2) -,exp (c) For the case a1=0, a2=1, b1=l and b2=2, we have the likelihood ratio is (+1)/2 x<0 2e(1-3c)/2 0<<1 2e(--1) as shown in the figure 3.5 0.5 Sectio 3. We are are to use the standard zero-one classification cost, that is A11=X22=0 dA12=入 【实例截图】
【核心代码】
《模式分类》duda第二版所有课后习题的解答，全英文，清晰版，
Preface In writing this Solution Manual I havc lcarncd a vcry important lesson. As a student, I thought that the best way to master a subject was to go to a superb university and study with an established expert. Later, I realized instead that the best way was to teach a course on the subject. Yet later, I was convinced that the best way was to write a detailed and extensive textbook. Now I know that all these years I have been wrong: in fact the best way to master a subject is to write the Solution manual In solving the problems for this Manual I have been forced to confront myriad technical details that might have tripped up the unsuspecting student. Students and teachers can thank me for simplifying or screening out problems that required pages of unenlightening calculations. Occasionally I had to go back to the text and dclctc the word“ easily” from problem references that read“ it can easily be shown( Proble….).” Throughout, I have tried to choose data or problem conditions that are particularly instructive. In solving these problems, I have found errors in early drafts of this text (as well as errors in books by other authors and even in classic refereed papers), and thus the accompanying text has been improved for the writing of this Manual I have tried to make the problem solutions self-contained and self-explanatory have gone to great lengths to ensure that the solutions are correct and clearly presented many have been reviewed by students in several classes. Surely there are errors and typos in this manuscript, but rather than editing and rechecking these solutions over months or even ycars, I thought it best to distribute the Manual, howevcr flawed as early as possible. I accept responsibility for these inevitable errors, and humbly ask anyone finding them to contact me directly.(Please, however, do not ask me to explain a solution or help you solve a problem! It should be a small matter to change the Manual for future printings, and you should contact the publisher to check that you have the most recent version. Notice, too, that this Manual contains a list of known typos and errata in the text which you might wish to photocopy and distribute to students I have tried to be thorough in order to help students, even to the occassional fault of verbosity. You will notice that several problems have the simple"explain your answer in words and"graph your results. These were added for students to gain intuition and a deeper understanding. Graphing per se is hardly an intellectual challenge, but if the student graphs functions, he or she will develop intuition and remember problem and its results better. Furthermore, when the student later sees graphs of data from dissertation or research work, the link to the homework problem and the material in the text will be more readily apparent. Note that due to the vaga.ries of automatic typesetting, figures may appear on pages after their reference in this Manual; be sure to consult the full solution to any problem I have also included worked examples and so sample final exams with solutions to cover materia l in text. I distribute a list of important equations(without descriptions) with the exam so students can focus understanding and using equations, rather than memorizing them. I also include on every final exam one problem verbatim from a homework. taken from the book. i find this motivates students to review carefull their homework assignments, and allows somewhat more difficult problems to be in- cluded. These will be updated and expanded; thus if you have exam questions you find particularly appropriate, alld would like to share theIm, please send a copy(witll solutions) to me It should be noted, too, that a set of overhead transparency masters of the figures om the text are available to faculty adopters. I have found these to be invaluable for lecturing, and I put a set on reserve in the library for students. The files can be accessed through a standard web browser or an ftp client program at the Wiley stm ftp area at ftp: //ftp. wiley. com/p ch_med/pattern/ or from a link on the Wiley Electrical Engineering software supplements page at http://www.wiley.com/products/subject/engineering/electrical/ software_supplem_elec_eng. html I have taught. from the text (in various st ages of completion) at the Universit of California at Berkeley(Extension Division) and in three Departments at Stan ford University: Electrical Engineering, Statistics and Computer Science. Numerous students and colleagues have made suggestions. Especially noteworthy in this re- gard are Sudeshna Adak, Jian An, Sung-Hyuk Cha, Koichi Ejiri, Rick Guadette John Heumann, Travis Kopp, Yaxin Liu, Yunqian Ma Sayan Mukhcrjcc, Hirobumi Nishida, Erhan Oztop, Steven Rogers, Charles Roosen, Sergio Bermejo Sanchez, God fried Toussaint, Namrata Vaswani, Mohammed Yousuf and Yu Zhong. Thanks too go to Dick Duda who gave several excellent suggestions I would greatly appreciate notices of any errors in this Manual or the text itself. I would be especially grateful for solutions to problems not yet solved. Please send any Buch information to me at the below address. I will incorporate them into subsequent leases of this manual This manual is for the use of educators and must not be distributed in bulk to students in any form. Short excerpts may be photocopied and distributed, but only in conjunction with the use of Pattern Classification(2nd ed.) I wish you all the best of luck in teaching and research Ricoh innovations. Inc David G. Stork 2882 Sand hill road suite 115 Menlo Park. CA 94025-7022 USA storkgrii. ricoh. com Contents Pref 1 Introduction 2 Bayesian decision theory Problem sol Computer Exercises 74 3 Maximum likelihood and Bayesian parameter estimation 77 Problern solutions Computer exercises 130 4 Nonparametric techniques 131 Problem solu 131 Computer Exercises 174 5 Linear discriminant functions 177 Problcm solutio 177 Computer Exercises 217 6 Multilayer neural networks 219 Problem solutio 219 Computer Exercises 254 7 Stochastic rmethods 255 Problem solut ic 25.5 Computer Exercises 276 8 Nonmetric methods 277 Problern solutions 277 Computer Exercises 4 9 Algorithm-independent machine learning 295 Problem solutions 295 Computer exercises 304 10 Unsupervised learning and clustering 305 Problcm solutions 305 Conputer Exercises ..,..355 Sample final exams and solutions 357 CONTENTS Worked examples 415 Errata and ammendations in the text 417 First and second printings .417 Fifth printing 443 Chapter 1 Introduction Problem solutions There are neither problems nor computer exercises in Chapter 1 CHAPTER 1 INTRODUCTION Chapter 2 Bayesian decision theory Problem solutions Section 2.1 1. Equation 7 in the text states P(error )=minPw1c), P(w2l c) (a) We assume, without loss of generality, that for a given particular we have P(w2l c)> P(wllc), and thus P(error r)=P(wllr). We have, Moreover, the normalization condition P(w1n)=1-P(w2 r). Together these imply P(w2 c)> 1/2 or 2P(w2x)>1 and 2P(w2 )P(w1lr)> P(w1 r)=P(errorr) This is true at every and hence the integrals obey 2P(w2r)P(wila)da Plerrorl)d In short, 2P(w2c)P(wila) provides an upper bound for P(errorr (b) From part(a), wc havc that P(w2 ac)>1/ 2, but in th currcnt conditions not greater than 1/a for a 2. Take as an example, a=4 3 and P(w1a)=0.4 and hence P(w2)=0.6. In this case, P(errorr)=0.4. Moreover, we have aP(x1|-)P(u2x)-4/3×0.6×0.4<P( erTor|x) This does not provide an upper bound for all values of P(w1lac (c)Let P(error c)=P(w1l ). In that case, for all we have P(w2r)P(wia)< P(wia)p(error a 2f(1 vie)p(errorc)d.c and we have a lower bound CHAPTER 2. BAYESIAN DECISION THEORY (d) The solution to part(b) also applies here Section 2.2 2. Wc arc given that the dcnsity is of the form p(alwi) (a) We seek k so that the function is normalized, as required by a true density. We integrate this function, set, it to 1.0 k exp[(a-ai)/bi]dr+ expl(a-ai)/b, ]dx[=1 which yields 20; h=1 ork= 1/(20i). Note that the 1lormlalization is independent of ai, which corresponds to a shift, along t he axis and is hence indeed irrelevant. to normalization The distribution is therefore written (b The likelihood ratio can be written directly p(x|1)b2 p(x|2) -,exp (c) For the case a1=0, a2=1, b1=l and b2=2, we have the likelihood ratio is (+1)/2 x<0 2e(1-3c)/2 0<<1 2e(--1) as shown in the figure 3.5 0.5 Sectio 3. We are are to use the standard zero-one classification cost, that is A11=X22=0 dA12=入 【实例截图】
【核心代码】
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