Ulisses Braga-Neto

Fundamentals of Pattern Recognition and Machine Learning (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 357
• Erscheinungstermin: 10. September 2020
• Englisch
• ISBN-13: 9783030276560
• Artikelnr.: 62299175

Autorenporträt

Ulisses Braga-Neto, Ph.D. is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. His main research areas are pattern recognition, machine learning, statistical signal processing, and applications in bioinformatics and materials informatics. He has worked extensively in the field of error estimation for pattern recognition and machine learning, having received an NSF CAREER award for research in this area, and co-authored a monograph with Edward R. Dougherty on the topic. He has also made contributions to the field of Mathematical morphology in signal and image processing.

Inhaltsangabe

Introduction.- Optimal Classification.- Sample-Based Classification.- Parametric Classification.- Nonparametric Classification.- Function-Approximation Classification.- Error Estimation for Classification.- Model Selection for Classification.- Dimensionality Reduction.- Clustering.- Regression.- Bayesian Machine Learning.- Scientific.- Machine Learning.- Appendices.