Preference Learning (eBook)

Johannes Fürnkranz, Eyke Hüllermeier (Herausgeber)

eBook Download: PDF

2010 | 2011
IX, 466 Seiten
Springer Berlin (Verlag)
978-3-642-14125-6 (ISBN)

Infos

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.

Inhalt

Preference Learning 4
Preface 6
Contents 8
Preference Learning: An Introduction 11
A Preference Optimization Based Unifying Frameworkfor Supervised Learning Problems 28
Part I Label Ranking 52
Part II Instance Ranking 132
Part III Object Ranking 184
Part IV Preferences in Multi-Attribute Domains 250
Part V Preferences in Information Retrieval 333
Part VI Preferences in Recommender Systems 380
Subject Index 448
Author Index 456

Produkt-Details

• Erscheint lt. Verlag: 19.11.2010
• Zusatzinfo: IX, 466 p.
• Verlagsort: Berlin
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Schlagworte: Artificial Intelligence • Data Mining • Information Retrieval • Instance ranking • Label ranking • learning • machine learning • Multicriteria decision-making • object ranking • Operations Research • preference learning • Preference prediction • Reasoning • Recommender Systems • Supevised learning
• ISBN-10: 3-642-14125-0 / 3642141250
• ISBN-13: 978-3-642-14125-6 / 9783642141256