Kaspar Riesen

Dieses Buch ist der ideale Begleiter, wenn Sie in einem Semester Java lernen möchten. Der Inhalt wurde gezielt ausgewählt, so dass nur die Konzepte besprochen werden, die Sie zum Einstieg in die Programmierung wirklich benötigen. Diese Konzepte werden mit zahlreichen, anschaulichen Beispielen illustriert. Weiter wird im Buch ein durchgehendes Beispielprojekt in Java, das von Kapitel zu Kapitel wächst, entwickelt. Zu jedem Kapitel finden sich zudem viele Aufgaben zur Selbstkontrolle und Programmierübungen in Java. Zu jeder Übung ist ein Lernvideo verlinkt, auf dem der Autor des Buches die Übung vorprogrammiert. Das Buch ist somit optimal geeignet für Studierende der Wirtschaftsinformatik, Informatik oder anderer Fachrichtungen sowie für alle Interessierten, die ohne Vorkenntnisse Programmieren lernen möchten.

This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED). The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm; describes a reformulation of GED to a quadratic assignment problem; illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem; reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework; examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time; includes appendices listing the datasets employed for the experimental evaluations discussedin the book.

This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED). The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm; describes a reformulation of GED to a quadratic assignment problem; illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem; reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework; examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time; includes appendices listing the datasets employed for the experimental evaluations discussedin the book.

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.