Improved Classification Rates for Localized Algorithms under Margin Conditions
Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chun
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Improved Classification Rates for Localized Algorithms under Margin Conditions
Improved Classification Rates for Localized Algorithms under Margin Conditions
Ingrid Karin Blaschzyk
Improved Classification Rates for Localized Algorithms under Margin Conditions
Ingrid Karin Blaschzyk Stuttgart, Germany Dissertation University of Stuttgart, 2019 D93
ISBN 978-3-658-29590-5 ISBN 978-3-658-29591-2 (eBook) https://doi.org/10.1007/978-3-658-29591-2 © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer Spektrum imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany
Danksagung An meinen Doktorvater Prof. Dr. Ingo Steinwart. Danke f¨ ur deine Zeit und das Vertrauen, dass du in mich gesetzt hast. Danke, dass du mir so Vieles w¨ahrend meiner Promotion erm¨oglicht hast. Ich hatte durch Workshops, Summer Schools und Konferenzen die M¨oglichkeit, das Forscher-Dasein zu erleben und konnte mich durch die Teilnahme an Mentoring-Programmen nicht nur fachlich weiterentwickeln. Danke auch f¨ ur die Unterst¨ utzung bei meinem Auslandsaufenthalt in Genua, Italien, durch den ich neuen Antrieb gewonnen habe. All dies ist nicht selbstverst¨andlich. An meine Gutachter Prof. Dr. Andreas Christmann und Prof. Dr. Philipp Hennig. Vielen Dank, dass Sie sich die Zeit genommen haben, um diese Arbeit zu lesen und zu bewerten. An die International Max Planck Research School for Intelligent Systems (IMPRS-IS). Danke f¨ ur die Aufnahme in diese Research School und die M¨oglichkeit, mich mit jungen Forschern aus unterschiedlichen Fachrichtungen auszutauschen. An meine (ehemaligen) Kollegen des ISA & friends. Danke f¨ ur diese wahnsinnig coole Zeit an der Uni und die gute Stimmun
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