Introduction to Statistical Pattern RecognitionThis completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises. |
Contents
| 1 | |
| 11 | |
| 51 | |
Chapter 4 PARAMETRIC CLASSIFIERS | 124 |
Chapter 5 PARAMETER ESTIMATION | 181 |
Chapter 6 NONPARAMETRIC DENSITY ESTIMATION | 254 |
Chapter 7 NONPARAMETRIC CLASSIFICATION AND ERROR ESTIMATION | 300 |
Chapter 8 SUCCESSIVE PARAMETER ESTIMATION | 367 |
Chapter 9 FEATURE EXTRACTION AND LINEAR MAPPING FOR SIGNAL REPRESENTATION | 399 |
Other editions - View all
Common terms and phrases
algorithm applied approach approximation assume Bayes error becomes bias bound boundary calculated called Chapter classifier close clusters components computed condition corresponding covariance matrix criterion decision density estimate density function depending derivatives determined dimensionality discriminant discussed distance effect eigenvalues eigenvectors equal Equation estimate example expansion expected vector Experiment expressed Figure follows given gives hand increases independent indicates iterative kernel larger linear linear classifier mapping mean measure method minimize node nonparametric normal distributions Note observation obtained optimal optimum parameters Parzen performance positive possible probability problem procedure properties quadratic classifier random Repeat respect rule sample mean samples satisfy selection separability shown shows space Table technique term threshold tion transformation true variables variance vector zero
Popular passages
Page 15 - R. 0. Duda and PE Hart, Pattern Classification and Scene Analysis, Wiley, 1972.
Page 43 - Since the determinant of the product of matrices is the product of the determinants...
Page 7 - Thus, pattern recognition, or decision-making in a broader sense, may be considered as a problem of estimating density functions in a high-dimensional space and dividing the space into the regions of categories or classes.