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Fundamentals of pattern recognition ...

Braga-Neto, Ulisses.

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Fundamentals of pattern recognition and machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fundamentals of pattern recognition and machine learning/ by Ulisses Braga-Neto.
Author:	Braga-Neto, Ulisses.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xxi, 400 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Pattern recognition systems. -
Online resource:	https://doi.org/10.1007/978-3-031-60950-3
ISBN:	9783031609503

Fundamentals of pattern recognition and machine learning
Braga-Neto, Ulisses.

Fundamentals of pattern recognition and machine learning[electronic resource] /by Ulisses Braga-Neto. - Second edition. - Cham :Springer International Publishing :2024. - xxi, 400 p. :ill., digital ;24 cm.

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.

This book is a concise but thorough introduction to 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 deep neural networks and Gaussian process regression. The Second Edition is thoroughly revised, featuring a new chapter on the emerging topic of physics-informed machine learning and additional material on deep neural networks. 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.

ISBN: 9783031609503

Standard No.: 10.1007/978-3-031-60950-3doiSubjects--Topical Terms:

527885
Pattern recognition systems.

LC Class. No.: TK7882.P3 / B73 2024

Dewey Class. No.: 006.4

Fundamentals of pattern recognition and machine learning
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505 0 $a 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.
520 $a This book is a concise but thorough introduction to 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 deep neural networks and Gaussian process regression. The Second Edition is thoroughly revised, featuring a new chapter on the emerging topic of physics-informed machine learning and additional material on deep neural networks. 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.
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650 2 4 $a Automated Pattern Recognition. $3 3538549
650 2 4 $a Bioinformatics. $3 553671
650 2 4 $a Computer Vision. $3 3538524
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