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OverviewHilbert space theory is an invaluable mathematical tool in numerous signal processing and systems theory applications. Hilbert spaces satisfying certain additional properties are known as Reproducing Kernel Hilbert Spaces (RKHSs). This primer gives a gentle and novel introduction to RKHS theory. It also presents several classical applications. It concludes by focusing on recent developments in the machine learning literature concerning embeddings of random variables. Parenthetical remarks are used to provide greater technical detail, which some readers may welcome, but they may be ignored without compromising the cohesion of the primer. Proofs are there for those wishing to gain experience at working with RKHSs; simple proofs are preferred to short, clever, but otherwise uninformative proofs. Italicised comments appearing in proofs provide intuition or orientation or both. A Primer on Reproducing Kernel Hilbert Spaces empowers readers to recognize when and how RKHS theory can profit them in their own work. Full Product DetailsAuthor: Jonathan H. Manton , Pierre-Olivier AmblardPublisher: now publishers Inc Imprint: now publishers Inc Dimensions: Width: 15.60cm , Height: 0.80cm , Length: 23.40cm Weight: 0.213kg ISBN: 9781680830927ISBN 10: 1680830929 Pages: 144 Publication Date: 18 December 2015 Audience: College/higher education , Postgraduate, Research & Scholarly Format: Paperback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1: Introduction 2: Finite-dimensional RKHSs 3: Function Spaces 4: Infinite-dimensional RKHSs 5: Geometry by Design 6: Applications to Linear Equations and Optimisation 7: Applications to Stochastic Processes 8: Embeddings of Random Realisations 9: Applications of Embeddings ReferencesReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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