Grohs Philipp (88 results)

hardcover. Condition: Very Good. In stock ready to dispatch from the UK.

235 mm x 155 mm, 0 g. XV, 258 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Applied and Numerical Harmonic Analysis ; 68. Sprache: Englisch.…

Paperback. Condition: New. View the abstract.

Paperback. Condition: Brand New. 93 pages. In Stock.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New. pp. 232.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volume explores the connection between the theoretical aspects of harmonic analysis and the construction of advanced multiscale representations that have emerged in signal and image processing. It highlights some of the most promising mathematical developments in harmonic analysis in the last decade brought about by the interplay among different areas of abstract and applied mathematics. This intertwining of ideas is considered starting from the theory of unitary group representations and leading to the construction of very efficient schemes for the analysis of multidimensional data.After an introductory chapter surveying the scientific significance of classical and more advanced multiscale methods, chapters cover such topics asAn overview of Lie theory focused on common applications in signal analysis, including the wavelet representation of the affine group, the Schrödinger representation of the Heisenberg group, and the metaplectic representation of the symplectic groupAn introduction to coorbit theory and how it can be combined with the shearlet transform to establish shearlet coorbit spacesMicrolocal properties of the shearlet transform and its ability to provide a precise geometric characterization of edges and interface boundaries in images and other multidimensional dataMathematical techniques to construct optimal data representations for a number of signal types, with a focus on the optimal approximation of functions governed by anisotropic singularities.A unified notation is used across all of the chapters to ensure consistency of the mathematical material presented.Harmonic and Applied Analysis: From Groups to Signals is aimed at graduate students and researchers in the areas of harmonic analysis and applied mathematics, as well as at other applied scientists interested in representations of multidimensional data. It can also be used as a textbookfor graduate courses in applied harmonic analysis.…

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volume explores the connection between the theoretical aspects of harmonic analysis and the construction of advanced multiscale representations that have emerged in signal and image processing. It highlights some of the most promising mathematical developments in harmonic analysis in the last decade brought about by the interplay among different areas of abstract and applied mathematics. This intertwining of ideas is considered starting from the theory of unitary group representations and leading to the construction of very efficient schemes for the analysis of multidimensional data.After an introductory chapter surveying the scientific significance of classical and more advanced multiscale methods, chapters cover such topics asAn overview of Lie theory focused on common applications in signal analysis, including the wavelet representation of the affine group, the Schrödinger representation of the Heisenberg group, and the metaplectic representation of the symplectic groupAn introduction to coorbit theory and how it can be combined with the shearlet transform to establish shearlet coorbit spacesMicrolocal properties of the shearlet transform and its ability to provide a precise geometric characterization of edges and interface boundaries in images and other multidimensional dataMathematical techniques to construct optimal data representations for a number of signal types, with a focus on the optimal approximation of functions governed by anisotropic singularities.A unified notation is used across all of the chapters to ensure consistency of the mathematical material presented.Harmonic and Applied Analysis: From Groups to Signals is aimed at graduate students and researchers in the areas of harmonic analysis and applied mathematics, as well as at other applied scientists interested in representations of multidimensional data. It can also be used as a textbookfor graduate courses in applied harmonic analysis.…

Condition: New.

Condition: New.

Hardcover. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condition: As New. Unread book in perfect condition.

Condition: New. 2022. New. Hardcover. . . . . .

Gebundene Ausgabe. Condition: Neu. This contributed volume explores the connection between the theoretical aspects of harmonic analysis and the construction of advanced multiscale representations that have emerged in signal and image processing. It highlights some of the most promising mathematical developments in harmonic analysis in the last decade brought about by the interplay among different areas of abstract and applied mathematics. This intertwining of ideas is considered starting from the theory of unitary group representations and leading to the construction of very efficient schemes for the analysis of multidimensional data.After an introductory chapter surveying the scientific significance of classical and more advanced multiscale methods, chapters cover such topics asAn overview of Lie theory focused on common applications in signal analysis, including the wavelet representation of the affine group, the Schr?dinger representation of the Heisenberg group, and the metaplectic representation of the symplectic groupAn introduction to coorbit theory and how it can be combined with the shearlet transform to establish shearlet coorbit spacesMicrolocal properties of the shearlet transform and its ability to provide a precise geometric characterization of edges and interface boundaries in images and other multidimensional dataMathematical techniques to construct optimal data representations for a number of signal types, with a focus on the optimal approximation of functions governed by anisotropic singularities.A unified notation is used across all of the chapters to ensure consistency of the mathematical material presented.Harmonic and Applied Analysis: From Groups to Signals is aimed at graduate students and researchers in the areas of harmonic analysis and applied mathematics, as well as at other applied scientists interested in representations of multidimensional data. It can also be used as a textbookfor graduate courses in applied harmonic analysis.? Sprache: Englisch Gewicht in Gramm: 574.…

Condition: New.

Condition: New.

Condition: New.

Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.

Taschenbuch. Condition: Neu. Harmonic and Applied Analysis | From Groups to Signals | Stephan Dahlke (u. a.) | Taschenbuch | Applied and Numerical Harmonic Analysis | xv | Englisch | 2016 | Birkhäuser | EAN 9783319355962 | Verantwortliche Person für die EU: Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condition: Brand New. reprint edition. 256 pages. 9.25x6.10x0.64 inches. In Stock.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New. In English.

Hardback. Condition: New. In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.…

Hardback. Condition: New. New copy - Usually dispatched within 3 working days.

Condition: New. 2022. New. Hardcover. . . . . . Books ship from the US and Ireland.

Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.…

Hardcover. Condition: New. BRAND NEW. book.

Gebunden. Condition: New. Inhaltsverzeichnis1. The modern mathematics of deep learning Julius Berner, Philipp Grohs, Gitta Kutyniok and Philipp Petersen 2. Generalization in deep learning Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio 3. Expressivity.