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020 _a9783031609503
_9978-3-031-60950-3
024 7 _a10.1007/978-3-031-60950-3
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
100 1 _aBraga-Neto, Ulisses.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982006
245 1 0 _aFundamentals of Pattern Recognition and Machine Learning
_h[electronic resource] /
_cby Ulisses Braga-Neto.
250 _a2nd ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXXI, 400 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- 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 _aThis 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.
650 0 _aImage processing
_xDigital techniques.
_964228
650 0 _aComputer vision.
650 0 _aMachine learning.
_955059
650 0 _aPattern recognition systems.
_965547
650 0 _aBioinformatics.
_964050
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_965550
650 2 4 _aMachine Learning.
_955059
650 2 4 _aAutomated Pattern Recognition.
_965551
650 2 4 _aBioinformatics.
_964050
650 2 4 _aComputer Vision.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031609497
776 0 8 _iPrinted edition:
_z9783031609510
776 0 8 _iPrinted edition:
_z9783031609527
856 4 0 _uhttps://doi.org/10.1007/978-3-031-60950-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
999 _c62288
_d62288