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hardcover. Zustand: Very Good.
Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbHardcover. Zustand: Brand New. 2nd edition. 400 pages. 9.25x6.10x10.00 inches. In Stock.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.
Sprache: Englisch
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Anbieter: moluna, Greven, Deutschland
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Sprache: Englisch
Verlag: Springer International Publishing, Springer Nature Switzerland Aug 2024, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Buch. Zustand: Neu. Neuware -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 not Elektronisches Buch, which can be downloaded from the book website.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 424 pp. Englisch.
Sprache: Englisch
Verlag: Springer International Publishing, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a concise but thorough introduction tothe 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 levelclassroom 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 not Elektronisches Buch, which can be downloaded from the book website.
Buch. Zustand: Neu. Fundamentals of Pattern Recognition and Machine Learning | Ulisses Braga-Neto | Buch | xxi | Englisch | 2024 | Springer | EAN 9783031609497 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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In den WarenkorbZustand: New. 336.
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In den WarenkorbZustand: New. 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 more specialized classifiers, it covers important topics and essential issues pertaining to the scientific val.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 336 pages. 10.00x6.50x1.00 inches. In Stock.
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In den WarenkorbZustand: New. Num Pages: 336 pages, illustrations. BIC Classification: TCB; TJ; UYQP. Category: (P) Professional & Vocational. Dimension: 245 x 164 x 26. Weight in Grams: 710. . 2015. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
Buch. Zustand: Neu. Neuware - 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 problemsThis 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).
Verlag: MCT / INPE, Rio de Janeiro, 2007
ISBN 10: 8517000323 ISBN 13: 9788517000324
Anbieter: Biblioteca de Babel, São Paulo, SP, Brasilien
Erstausgabe
Hardcover. Zustand: Fine. 1st Edition. Rio de Janeiro, RJ, Brazil, October 10-13, 2007. 24x15cm, xiv + 475p. Part I: Lattice theory; Part II: Geometry and topology; Part III: Signal processing; Part IV: Image processing; Part V: Connectivity; Part VI: Watershed segmentation; Part VII: Texture and geometrical segmentation; Part VIII: Algorithms and architectures [cisa].