Error Estimation for Pattern Recognition 1st Edition by Ulisses Braga Neto, Edward Dougherty 9781119079378 1119079373

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ISBN 10: 1119079373
ISBN 13: 9781119079378 
Author: Ulisses M. Braga Neto; Edward R. Dougherty

This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification. Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian estimators. This book was motivated by the application of pattern recognition to high-throughput data with limited replicates, which is a basic problem now appearing in many areas. The first two chapters cover basic issues in classification error estimation, such as definitions, test-set error estimation, and training-set error estimation. The remaining chapters in this book cover results on the performance and representation of training-set error estimators for various pattern classifiers. Additional features of the book include: • The latest results on the accuracy of error estimation • Performance analysis of re-substitution, cross-validation, and bootstrap error estimators using analytical and simulation approaches • Highly interactive computer-based exercises and end-of-chapter problems This is the first book exclusively about error estimation for pattern recognition. Ulisses M. Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, USA. He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University. Dr. Braga Neto received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing. He is an IEEE Senior Member. Edward R. Dougherty is a Distinguished Professor, Robert F. Kennedy ’26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, USA. He is a fellow of both the IEEE and SPIE, and he has received the SPIE Presidents Award. Dr. Dougherty has authored several books including Epistemology of the Cell: A Systems Perspective on Biological Knowledge and Random Processes for Image and Signal Processing (Wiley-IEEE Press).

Error Estimation for Pattern Recognition 1st Table of contents:

CHAPTER 1 CLASSIFICATION
1.1 CLASSIFIERS
1.2 POPULATION-BASED DISCRIMINANTS
1.3 CLASSIFICATION RULES
1.4 SAMPLE-BASED DISCRIMINANTS
1.5 HISTOGRAM RULE
1.6 OTHER CLASSIFICATION RULES
1.7 FEATURE SELECTION
EXERCISES
NOTES

CHAPTER 2 ERROR ESTIMATION
2.1 ERROR ESTIMATION RULES
2.2 PERFORMANCE METRICS
2.3 TEST-SET ERROR ESTIMATION
2.4 RESUBSTITUTION
2.5 CROSS-VALIDATION
2.6 BOOTSTRAP
2.7 CONVEX ERROR ESTIMATION
2.8 SMOOTHED ERROR ESTIMATION
2.9 BOLSTERED ERROR ESTIMATION
EXERCISES
NOTES

CHAPTER 3 PERFORMANCE ANALYSIS
3.1 EMPIRICAL DEVIATION DISTRIBUTION
3.2 REGRESSION
3.3 IMPACT ON FEATURE SELECTION
3.4 MULTIPLE-DATA-SET REPORTING BIAS
3.5 MULTIPLE-RULE BIAS
3.6 PERFORMANCE REPRODUCIBILITY
EXERCISES
NOTES

CHAPTER 4 ERROR ESTIMATION FOR DISCRETE CLASSIFICATION
4.1 ERROR ESTIMATORS
4.2 SMALL-SAMPLE PERFORMANCE
4.3 LARGE-SAMPLE PERFORMANCE
EXERCISES

CHAPTER 5 DISTRIBUTION THEORY
5.1 MIXTURE SAMPLING VERSUS SEPARATE SAMPLING
5.2 SAMPLE-BASED DISCRIMINANTS REVISITED
5.3 TRUE ERROR
5.4 ERROR ESTIMATORS
5.5 EXPECTED ERROR RATES
5.6 HIGHER-ORDER MOMENTS OF ERROR RATES
5.7 SAMPLING DISTRIBUTION OF ERROR RATES
EXERCISES

CHAPTER 6 GAUSSIAN DISTRIBUTION THEORY: UNIVARIATE CASE
6.1 HISTORICAL REMARKS
6.2 UNIVARIATE DISCRIMINANT
6.3 EXPECTED ERROR RATES
6.4 HIGHER-ORDER MOMENTS OF ERROR RATES
6.5 SAMPLING DISTRIBUTIONS OF ERROR RATES
EXERCISES

CHAPTER 7 GAUSSIAN DISTRIBUTION THEORY: MULTIVARIATE CASE
7.1 MULTIVARIATE DISCRIMINANTS
7.2 SMALL-SAMPLE METHODS
7.3 LARGE-SAMPLE METHODS
EXERCISES
NOTES

CHAPTER 8 BAYESIAN MMSE ERROR ESTIMATION
8.1 THE BAYESIAN MMSE ERROR ESTIMATOR
8.2 SAMPLE-CONDITIONED MSE
8.3 DISCRETE CLASSIFICATION
8.4 LINEAR CLASSIFICATION OF GAUSSIAN DISTRIBUTIONS
8.5 CONSISTENCY
8.6 CALIBRATION
8.7 CONCLUDING REMARKS
EXERCISES
NOTES

APPENDIX A BASIC PROBABILITY REVIEW
A.1 SAMPLE SPACES AND EVENTS
A.2 DEFINITION OF PROBABILITY
A.3 BOREL-CANTELLI LEMMAS
A.4 CONDITIONAL PROBABILITY
A.5 RANDOM VARIABLES
A.6 DISCRETE RANDOM VARIABLES
A.7 EXPECTATION
A.8 CONDITIONAL EXPECTATION
A.9 VARIANCE
A.10 VECTOR RANDOM VARIABLES
A.11 THE MULTIVARIATE GAUSSIAN
A.12 CONVERGENCE OF RANDOM SEQUENCES
A.13 LIMITING THEOREMS

APPENDIX B VAPNIK–CHERVONENKIS THEORY
B.1 SHATTER COEFFICIENTS
B.2 THE VC DIMENSION
B.3 VC THEORY OF CLASSIFICATION
B.4 VAPNIK–CHERVONENKIS THEOREM

APPENDIX C DOUBLE ASYMPTOTICS

BIBLIOGRAPHY
AUTHOR INDEX
SUBJECT INDEX
EULA

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Ulisses Braga Neto,Edward Dougherty,Error Estimation