Instruction to Statistical Pattern Recognition

Keinosuke Fukunaga

1972年1月 · Elsevier

電子書

386

頁

符合資格

評分和評論未經驗證瞭解詳情

關於本電子書

Introduction to Statistical Pattern Recognition introduces the reader to statistical pattern recognition, with emphasis on statistical decision and estimation. Pattern recognition problems are discussed in terms of the eigenvalues and eigenvectors. Comprised of 11 chapters, this book opens with an overview of the formulation of pattern recognition problems. The next chapter is devoted to linear algebra, with particular reference to the properties of random variables and vectors. Hypothesis testing and parameter estimation are then discussed, along with error probability estimation and linear classifiers. The following chapters focus on successive approaches where the classifier is adaptively adjusted each time one sample is observed; feature selection and linear mapping for one distribution and multidistributions; and problems of nonlinear mapping. The final chapter describes a clustering algorithm and considers criteria for both parametric and nonparametric clustering. This monograph will serve as a text for the introductory courses of pattern recognition as well as a reference book for practitioners in the fields of mathematics and statistics.