Chazal frederic (37 risultati)

Condizione: good. This is a pre-loved book that shows moderate signs of wear from previous reading. You may notice creases, edge wear, or a cracked spine, but it remains in solid, readable condition. Please note: -May include library or rental stickers, stamps, or markings. -Supplemental materials e.g., CDs, access codes, inserts are not guaranteed. -Box sets may not come with the original outer box. If it does, the box will not be in perfect condition. Your satisfaction is our top priority! If you have any questions or concerns about your order, please don't hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading.…

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.…

Condizione: New.

Condizione: New.

Condizione: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,350grams, ISBN:9783319425436.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: Brand New. 233 pages. 9.00x6.00x0.50 inches. In Stock.

Condizione: New.

Condizione: New. 1st edition NO-PA16APR2015-KAP.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: New.

Condizione: New.

broschiert. 0. 120 S., broschiert, gut erhalten. 0.500 kg.

Condizione: New.

Condizione: New. In English.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In English.

Hardback. Condizione: New. Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.…

Condizione: New. pp. 130 1st ed. 2016 edition NO-PA16APR2015-KAP.

Paperback. Condizione: Brand New. 132 pages. 9.00x6.00x0.50 inches. In Stock.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a comprehensive treatment of the theory of persistence modules over the real line. It presents a set of mathematical tools to analyse the structure and to establish the stability of such modules, providing a sound mathematical framework for the study of persistence diagrams. Completely self-contained, this brief introduces the notion of persistence measure and makes extensive use of a new calculus of quiver representations to facilitate explicit computations.Appealing to both beginners and experts in the subject, The Structure and Stability of Persistence Modules provides a purely algebraic presentation of persistence, and thus complements the existing literature, which focuses mainly on topological and algorithmic aspects. …

Paperback. Condizione: New. Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.…

Hardcover. Condizione: Brand New. 233 pages. 9.00x6.25x0.75 inches. In Stock.

Taschenbuch. Condizione: Neu. The Structure and Stability of Persistence Modules | Frédéric Chazal (u. a.) | Taschenbuch | x | Englisch | 2016 | Springer | EAN 9783319425436 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Hardcover. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardback. Condizione: New. Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.…