The Resource Preference learning, Johannes Fürnkranz, Eyke Hüllermeier, editors, (electronic resource)

Preference learning, Johannes Fürnkranz, Eyke Hüllermeier, editors, (electronic resource)

Label
Preference learning
Title
Preference learning
Statement of responsibility
Johannes Fürnkranz, Eyke Hüllermeier, editors
Contributor
Provider
Subject
Genre
Language
eng
Summary
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 recent years. 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. Preference learning is concerned with the acquisition of preference models from data – it involves learning from observations that reveal information about the preferences of an individual or a class of individuals, and building models that generalize beyond such training data. 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 remainder of the book is organized into parts that follow the developed framework, complementing survey articles with in-depth treatises of current research topics in this area. 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
Member of
Cataloging source
GW5XE
Image bit depth
0
LC call number
Q325.5
LC item number
.P74 2010
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • SpringerLink
  • Fürnkranz, Johannes
  • Hüllermeier, Eyke
http://library.link/vocab/subjectName
  • Machine learning
  • Data mining
  • Computer science
  • Science
  • Informatique
  • Machine learning
  • COMPUTERS
  • COMPUTERS
  • Künstliche Intelligenz
  • Lerntechnik
  • Gewichtung
Label
Preference learning, Johannes Fürnkranz, Eyke Hüllermeier, editors, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Preference Learning: An Introduction -- A Preference Optimization Based Unifying Framework for Supervised Learning Problems -- Label Ranking Algorithms: A Survey -- Preference Learning and Ranking by Pairwise Comparison -- Decision Tree Modeling for Ranking Data -- Co-regularized Least-Squares for Label Ranking -- A Survey on ROC-Based Ordinal Regression -- Ranking Cases with Classification Rules -- A Survey and Empirical Comparison of Object Ranking Methods -- Dimension Reduction for Object Ranking -- Learning of Rule Ensembles for Multiple Attribute Ranking Problems -- Learning Lexicographic Preference Models -- Learning Ordinal Preferences on Multiattribute Domains: the Case of CP-nets -- Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models -- Learning Aggregation Operators for Preference Modeling -- Evaluating Search Engine Relevance with Click-Based Metrics -- Learning SVM Ranking Function from User Feedback Using Document -- Metadata and Active Learning in the Biomedical Domain -- Learning Preference Models in Recommender Systems -- Collaborative Preference Learning -- Discerning Relevant Model Features in a Content-Based Collaborative Recommender System -- Author Index -- Subject Index
Dimensions
unknown
Extent
1 online resource (ix, 466 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9783642141249
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
Stock number
978-3-642-14124-9
System control number
  • (OCoLC)694568717
  • (OCoLC)ocn694568717
Label
Preference learning, Johannes Fürnkranz, Eyke Hüllermeier, editors, (electronic resource)
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Preference Learning: An Introduction -- A Preference Optimization Based Unifying Framework for Supervised Learning Problems -- Label Ranking Algorithms: A Survey -- Preference Learning and Ranking by Pairwise Comparison -- Decision Tree Modeling for Ranking Data -- Co-regularized Least-Squares for Label Ranking -- A Survey on ROC-Based Ordinal Regression -- Ranking Cases with Classification Rules -- A Survey and Empirical Comparison of Object Ranking Methods -- Dimension Reduction for Object Ranking -- Learning of Rule Ensembles for Multiple Attribute Ranking Problems -- Learning Lexicographic Preference Models -- Learning Ordinal Preferences on Multiattribute Domains: the Case of CP-nets -- Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models -- Learning Aggregation Operators for Preference Modeling -- Evaluating Search Engine Relevance with Click-Based Metrics -- Learning SVM Ranking Function from User Feedback Using Document -- Metadata and Active Learning in the Biomedical Domain -- Learning Preference Models in Recommender Systems -- Collaborative Preference Learning -- Discerning Relevant Model Features in a Content-Based Collaborative Recommender System -- Author Index -- Subject Index
Dimensions
unknown
Extent
1 online resource (ix, 466 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9783642141249
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
Stock number
978-3-642-14124-9
System control number
  • (OCoLC)694568717
  • (OCoLC)ocn694568717

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