虫虫首页| 资源下载| 资源专辑| 精品软件
登录| 注册

Chapter

  • 最新的支持向量机工具箱

    最新的支持向量机工具箱,有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, Chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.

    标签: 支持向量机 工具箱

    上传时间: 2013-12-16

    上传用户:亚亚娟娟123

  • Software Testing, Second Edition provides practical insight into the world of software testing and q

    Software Testing, Second Edition provides practical insight into the world of software testing and quality assurance. Learn how to find problems in any computer program, how to plan an effective test approach and how to tell when software is ready for release. Updated from the previous edition in 2000 to include a Chapter that specifically deals with testing software for security bugs, the processes and techniques used throughout the book are timeless. This book is an excellent investment if you want to better understand what your Software Test team does or you want to write better software.

    标签: practical Software provides software

    上传时间: 2015-04-30

    上传用户:bjgaofei

  • A Java virtual machine instruction consists of an opcode specifying the operation to be performed, f

    A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This Chapter gives details about the format of each Java virtual machine instruction and the operation it performs.

    标签: instruction specifying operation performed

    上传时间: 2014-01-11

    上传用户:yiwen213

  • A Java virtual machine instruction consists of an opcode specifying the operation to be performed, f

    A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This Chapter gives details about the format of each Java virtual machine instruction and the operation it performs.

    标签: instruction specifying operation performed

    上传时间: 2015-05-02

    上传用户:daoxiang126

  • A Java virtual machine instruction consists of an opcode specifying the operation to be performed, f

    A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This Chapter gives details about the format of each Java virtual machine instruction and the operation it performs.

    标签: instruction specifying operation performed

    上传时间: 2015-05-02

    上传用户:shawvi

  • A Java virtual machine instruction consists of an opcode specifying the operation to be performed, f

    A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This Chapter gives details about the format of each Java virtual machine instruction and the operation it performs.

    标签: instruction specifying operation performed

    上传时间: 2013-12-12

    上传用户:朗朗乾坤

  • 十部经典算法合集 .chm Fundamentals of Data Structures by Ellis Horowitz and Sartaj Sahni PREFACE C

    十部经典算法合集 .chm Fundamentals of Data Structures by Ellis Horowitz and Sartaj Sahni PREFACE Chapter 1: INTRODUCTION Chapter 2: ARRAYS Chapter 3: STACKS AND QUEUES Chapter 4: LINKED LISTS Chapter 5: TREES Chapter 6: GRAPHS Chapter 7: INTERNAL SORTING Chapter 8: EXTERNAL SORTING Chapter 9: SYMBOL TABLES Chapter 10: FILES APPENDIX A: SPARKS APPENDIX B: ETHICAL CODE IN INFORMATION PROCESSING APPENDIX C: ALGORITHM INDEX BY Chapter

    标签: Fundamentals Structures Horowitz PREFACE

    上传时间: 2015-05-19

    上传用户:维子哥哥

  • MFC Black Book Introduction: Are you an MFC programmer? Good. There are two types of MFC programme

    MFC Black Book Introduction: Are you an MFC programmer? Good. There are two types of MFC programmers. What kind are you? The first kind are the good programmers who write programs that conform to the way MFC wants you to do things. The second bunch are wild-eyed anarchists who insist on getting things done their way. Me, I’m in the second group. If you are in the same boat (or would like to be) this book is for you. This book won’t teach you MFC—not in the traditional sense. You should pick it up with a good understanding of basic MFC programming and a desire to do things differently. This isn’t a Scribble tutorial (although I will review some fundamentals in the first Chapter). You will learn how to wring every drop from your MFC programs. You’ll discover how to use, abuse, and abandon the document/view architecture. If you’ve ever wanted custom archives, you’ll find that, too.

    标签: MFC Introduction programmer programme

    上传时间: 2015-05-30

    上传用户:youke111

  • Routine mampres: To obtain amplitude response from h(exp(jw)). input parameters: h :n dimensione

    Routine mampres: To obtain amplitude response from h(exp(jw)). input parameters: h :n dimensioned complex array. the frequency response is stored in h(0) to h(n-1). n :the dimension of h and amp. fs :sampling frequency (Hz). iamp:If iamp=0: The Amplitude Res. amp(k)=abs(h(k)) If iamp=1: The Amplitude Res. amp(k)=20.*alog10(abs(h(k))). output parameters: amp :n dimensioned real array. the amplitude-frequency response is stored in amp(0) to amp(n-1). Note: this program will generate a data file "filename.dat" . in Chapter 2

    标签: dimensione parameters amplitude response

    上传时间: 2013-12-19

    上传用户:xfbs821

  • Routine mar1psd: To compute the power spectum by AR-model parameters. Input parameters: ip : AR

    Routine mar1psd: To compute the power spectum by AR-model parameters. Input parameters: ip : AR model order (integer) ep : White noise variance of model input (real) ts : Sample interval in seconds (real) a : Complex array of AR parameters a(0) to a(ip) Output parameters: psdr : Real array of power spectral density values psdi : Real work array in Chapter 12

    标签: parameters AR-model Routine mar1psd

    上传时间: 2015-06-09

    上传用户:playboys0