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Combining theory and practice, this book is suitable for the graduate or advanced undergraduate level classroom and self-study. It fills the need of a mathematically-rigorous text that is relevant to the practitioner as well, with datasets from applications in bioinformatics and materials informatics 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 Keras/Tensorflow. All plots in the text were generated using python scripts and jupyter notebooks, which can be downloaded from the book website.
Combining theory and practice, this book is suitable for the graduate or advanced undergraduate level classroom and self-study. It fills the need of a mathematically-rigorous text that is relevant to the practitioner as well, with datasets from applications in bioinformatics and materials informatics 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 Keras/Tensorflow. All plots in the text were generated using python scripts and jupyter notebooks, which can be downloaded from the book website.
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.
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.
| Erscheinungsjahr: | 2024 |
|---|---|
| Fachbereich: | Anwendungs-Software |
| Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xxi
400 S. |
| ISBN-13: | 9783031609497 |
| ISBN-10: | 3031609492 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Braga-Neto, Ulisses |
| Auflage: | Second Edition 2024 |
| Hersteller: |
Springer Nature Switzerland
Springer International Publishing |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 260 x 183 x 29 mm |
| Von/Mit: | Ulisses Braga-Neto |
| Erscheinungsdatum: | 07.08.2024 |
| Gewicht: | 0,99 kg |
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.
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.
| Erscheinungsjahr: | 2024 |
|---|---|
| Fachbereich: | Anwendungs-Software |
| Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xxi
400 S. |
| ISBN-13: | 9783031609497 |
| ISBN-10: | 3031609492 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Braga-Neto, Ulisses |
| Auflage: | Second Edition 2024 |
| Hersteller: |
Springer Nature Switzerland
Springer International Publishing |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 260 x 183 x 29 mm |
| Von/Mit: | Ulisses Braga-Neto |
| Erscheinungsdatum: | 07.08.2024 |
| Gewicht: | 0,99 kg |
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