Supervised Learning: Mathematical Foundations and Real-world Applications
Autor Dalia Chakrabartyen Limba Engleză Hardback – 3 mar 2025
The generic solution to the problem of secured supervised learning amidst real-world messiness lies in treating the sought inter-variable relation as a (function-valued) random variable, which, being random, is ascribed a probability distribution. Then recalling that distributions on the space of functions is given by stochastic processes, the sought function is proposed to be a sample function of a stochastic process. This process is chosen as one that imposes minimal constraints on the sought function – identified as a Gaussian Process (GP) in the book. Thus, the sought function can be inferred upon, as long as the covariance function of the underlying GP is learnt, given the available training set. The book presents probabilistic techniques to undertake said learning, within the challenges borne by the data, and illustrates such techniques on real data. Learning of a function is always followed by closed-form prediction of the mean and dispersion of the output variable that is realised at a test input.
To help with the background, the book includes reviews on stochastic processes and basic probability theory. This makes it valuable for students across disciplines, including students of computational sciences, statistics, and mathematics.
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Specificații
ISBN-13: 9781032283302
ISBN-10: 1032283300
Pagini: 342
Ilustrații: 90
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
ISBN-10: 1032283300
Pagini: 342
Ilustrații: 90
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Public țintă
AcademicCuprins
Foreword Preface Acknowledgements 1. Inter-variable relationships 2. Bayesianism 3. Supervised learning & prediction, using Gaussian
Processes 4. Covariance kernels suitable for real-world data 5. Learning a high-dimensional function 6. A self-assembled prior on correlation matrices Bibliography Index
Processes 4. Covariance kernels suitable for real-world data 5. Learning a high-dimensional function 6. A self-assembled prior on correlation matrices Bibliography Index
Notă biografică
Dr. Dalia Chakrabarty is a Reader in Statistical Data Science in the Department of Mathematics at the University of York. Her PhD is from St. Cross College in the University of Oxford, and she works on the development of methods to permit the probabilistic learning of random variables of various kinds, given real world data that is diversely challenging.
Descriere
This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. This book is valuable for students across disciplines, including students of computational sciences, statistics, and mathematics.