03143nam a22004935i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001600153050001100169072001600180072002300196072001500219100008200234245011000316250001800426264007500444300003400519336002600553337002600579338003600605347002400641505038700665520108301052650004202135650002102177650002202198650003302220650002002253650006402273650002202337650003502359650002002394650002102414710003402435773002602469776003602495776003602531776003602567856004602603978-3-031-60950-3DE-He21320251030112841.0cr nn 008mamaa240807s2024    sz |    s    |||| 0|eng d  a97830316095037 a10.1007/978-3-031-60950-32doi 4aTA1501-1820 4aTA1634 7aUYT2bicssc 7aCOM0160002bisacsh 7aUYT2thema1 aBraga-Neto, Ulisses.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aFundamentals of Pattern Recognition and Machine Learningh[electronic resource] /cby Ulisses Braga-Neto.  a2nd ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXXI, 400 p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 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.  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. 0aImage processingxDigital techniques. 0aComputer vision. 0aMachine learning. 0aPattern recognition systems. 0aBioinformatics.14aComputer Imaging, Vision, Pattern Recognition and Graphics.24aMachine Learning.24aAutomated Pattern Recognition.24aBioinformatics.24aComputer Vision.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303160949708iPrinted edition:z978303160951008iPrinted edition:z978303160952740uhttps://doi.org/10.1007/978-3-031-60950-3