Predicting partial orders: ranking with abstention
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Uncertainty in clustering and classification
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Preference-based policy iteration: leveraging preference learning for reinforcement learning
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Ranking and 1-dimensional projection of cell development transcription profiles
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
Monotone instance ranking with MIRA
DS'11 Proceedings of the 14th international conference on Discovery science
Learning from label preferences
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Efficiently learning the preferences of people
Machine Learning
Learning community-based preferences via dirichlet process mixtures of Gaussian processes
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Editorial: Preference learning and ranking
Machine Learning
Semi-supervised learning on closed set lattices
Intelligent Data Analysis
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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.