Johannes Fürnkranz Reihenfolge der Bücher

Der Obere im kirchlichen Verwaltungsbeschwerdeverfahren – zwischen Effizienz der Verwaltung und Rechtsschutz im Verfahren. Can. 1739 CIC/1983 zählt sieben Entscheidungsmöglichkeiten über die Beschwerde auf. Ist der Spielraum des kirchlichen Oberen damit auf sieben mögliche Maßnahmen beschränkt? Handelt es sich um eine bloß beispielhafte Aufzählung, die dem Oberen völlige Handlungsfreiheit lässt? Welche Kriterien und Orientierungslinien sind dem Oberen zur Hand gegeben? Ekklesiologische und rechtstheoretische Grundlagen der hierarchischen Beschwerde und deren Umsetzung im kirchlichen Verwaltungsrecht (Subsidiaritätsprinzip, Rechtsschutzanliegen, konkrete Verfahrensschritte usw.) werden untersucht. Ebenso die strukturelle Position des hierarchischen Oberen im Gesamtgefüge des kirchlichen Verwaltungsaufbaus: Im Spannungsfeld zwischen bestmöglicher Effektivität der Verwaltung und notwendigem Schutz der Rechte der Gläubigen.

This book constitutes the proceedings of the 16th International Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances in the development and analysis of methods of automatic scientific knowledge discovery, machine learning, intelligent data analysis, and their application to knowledge discovery.

Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.

The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.

From the reviews: „In this book, we find many ways of representing machine learning from different fields, including active learning, algorithmic learning, case-based learning, classifier systems, clustering algorithms, decision-tree learning, inductive inference, kernel methods, knowledge discovery, multiple-instance learning, reinforcement learning, statistical learning, and support vector machines. Most of the current issues in machine learning research are discussed. … I strongly recommend this book for all researchers interested in the very best of machine learning studies.“ (Agliberto Cierco, ACM Computing Reviews, Vol. 49 (5), 2008)

The two premier annual European conferences in machine learning and data mining have been collocated since the joint conference in Freiburg, Germany, in 2001. The European Conference on Machine Learning began 20 years ago, following the first European Working Session on Learning in Orsay, France, in 1986. The conference continues to grow and remains vibrant. The European Conference on Principles and Practice of Knowledge Discovery in Databases marks its tenth anniversary, having started in 1997 in Trondheim, Norway. Over the years, the ECML/PKDD series has become one of the largest and most selective international conferences, uniquely serving as a common forum for these closely related fields. In 2006, the 6th collocated ECML/PKDD was held from September 18-22 at Humboldt-Universität zu Berlin, featuring the 17th European Conference on Machine Learning and the 10th European Conference on Knowledge Discovery in Databases. The hierarchical reviewing process introduced in 2005 was continued in 2006, with 32 Area Chairs overseeing various research topics based on submission statistics to ensure balanced workloads. For the first time, a joint Program Committee was formed, consisting of 280 esteemed researchers, primarily nominated by the Area Chairs.