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Author | : Ulisses M. Braga Neto |
Publisher | : John Wiley & Sons |
Total Pages | : 336 |
Release | : 2015-06-22 |
Genre | : Technology & Engineering |
ISBN | : 1119079373 |
Download Error Estimation for Pattern Recognition Book in PDF, ePub and Kindle
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).
Author | : Ulisses M. Braga Neto |
Publisher | : John Wiley & Sons |
Total Pages | : 336 |
Release | : 2015-06-17 |
Genre | : Technology & Engineering |
ISBN | : 1119079330 |
Download Error Estimation for Pattern Recognition Book in PDF, ePub and Kindle
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).
Author | : Ulisses Braga-Neto |
Publisher | : Springer Nature |
Total Pages | : 357 |
Release | : 2020-09-10 |
Genre | : Computers |
ISBN | : 3030276562 |
Download Fundamentals of Pattern Recognition and Machine Learning Book in PDF, ePub and Kindle
Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.
Author | : Tsvi Lissack |
Publisher | : |
Total Pages | : 49 |
Release | : 1972 |
Genre | : |
ISBN | : |
Download A Separability Measure for Feature Selection and Error Estimation in Pattern Recognition Book in PDF, ePub and Kindle
Author | : Tsvi Lissack |
Publisher | : |
Total Pages | : 150 |
Release | : 1973 |
Genre | : Error analysis (Mathematics) |
ISBN | : |
Download Error Estimation and Its Application to Feature Extraction in Pattern Recognition Book in PDF, ePub and Kindle
Author | : Keinosuke Fukunaga |
Publisher | : Elsevier |
Total Pages | : 606 |
Release | : 2013-10-22 |
Genre | : Computers |
ISBN | : 0080478654 |
Download Introduction to Statistical Pattern Recognition Book in PDF, ePub and Kindle
This 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.
Author | : Luc Devroye |
Publisher | : Springer Science & Business Media |
Total Pages | : 631 |
Release | : 2013-11-27 |
Genre | : Mathematics |
ISBN | : 1461207118 |
Download A Probabilistic Theory of Pattern Recognition Book in PDF, ePub and Kindle
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.
Author | : Stephen Jay Whitsitt |
Publisher | : |
Total Pages | : 200 |
Release | : 1977 |
Genre | : Automatic data collection systems |
ISBN | : |
Download Error Estimation and Separability Measures in Feature Selection for Multiclass Pattern Recognition Book in PDF, ePub and Kindle
Author | : Petrus Hendrik Zwart |
Publisher | : |
Total Pages | : 0 |
Release | : 2003 |
Genre | : |
ISBN | : |
Download Error Estimation and Pattern Recognition Techniques in Protein Crystallography Book in PDF, ePub and Kindle
Author | : Charles W. Therrien |
Publisher | : |
Total Pages | : 280 |
Release | : 1989-01-17 |
Genre | : Computers |
ISBN | : |
Download Decision Estimation and Classification Book in PDF, ePub and Kindle
Very Good,No Highlights or Markup,all pages are intact.