Partially Supervised Learning First IAPR TC3 Workshop, PSL 2011,
This book constitutes thoroughly refereed revised selected papers from the First IAPR TC3 Workshop on Partially Supervised Learning, PSL 2011, held in Ulm, Germany, in September 2011. The 14 papers presented in this volume were carefully reviewed and sele
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LNAI Founding Series Editor Joerg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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Friedhelm Schwenker Edmondo Trentin (Eds.)
Partially Supervised Learning First IAPR TC3 Workshop, PSL 2011 Ulm, Germany, September 15-16, 2011 Revised Selected Papers
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Series Editors Randy Goebel, University of Alberta, Edmonton, Canada Jörg Siekmann, University of Saarland, Saarbrücken, Germany Wolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany Volume Editors Friedhelm Schwenker University of Ulm Institute of Neural Information Processing 89069 Ulm, Germany E-mail: [email protected] Edmondo Trentin University of Siena DII – Dipartimento di Ingegneria dell’Informazione Via Roma 56, 53100 Siena, Italy E-mail: [email protected]
ISSN 0302-9743 e-ISSN 1611-3349 ISBN 978-3-642-28257-7 e-ISBN 978-3-642-28258-4 DOI 10.1007/978-3-642-28258-4 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2012930867 CR Subject Classification (1998): I.2.6, I.2, I.5, I.4, H.3, F.2.2, J.3 LNCS Sublibrary: SL 7 – Artificial Intelligence
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Preface
Partially supervised learning (PSL) is a rapidly evolving area of machine learning. In many applications unlabeled data may be relatively easy to collect, whereas labeling these data is difficult, expensive or/and time consuming as it needs the effort of human experts. PSL is a general framework for learning with labeled and unlabeled data, for instance, in classification, it is assumed that each learning sample consists of a feature vector and some information about its class. In the PSL framework this information might be a crisp label, or a label plus a confidence value, or it might be an imprecise and/or uncertain soft label defined through a certain type of uncertainty model (fuzzy, Dempster–Shafer), or it might be that information about a class label is not available. The PSL framework thus generalizes many kinds of learning paradigms including su
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