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| 003 - CONTROL NUMBER IDENTIFIER |
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OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20140310151119.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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101119s2011 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783642141256 |
|
978-3-642-14125-6 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q334-342 |
|
|---|
| Classification number |
TJ210.2-211.495 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3 |
| Edition number |
23 |
| 264 #1 - |
|---|
| -- |
Berlin, Heidelberg : |
| -- |
Springer Berlin Heidelberg : |
| -- |
Imprint: Springer, |
| -- |
2011. |
| 912 ## - |
|---|
| -- |
ZDB-2-SCS |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Fürnkranz, Johannes. |
| Relator term |
editor. |
| 245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Title |
Preference Learning |
| Medium |
[electronic resource] / |
| Statement of responsibility, etc |
edited by Johannes Fürnkranz, Eyke Hüllermeier. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
VIII, 454p. 81 illus. |
| Other physical details |
online resource. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer science. |
|
|---|
| Topical term or geographic name as entry element |
Data mining. |
|
|---|
| Topical term or geographic name as entry element |
Artificial intelligence. |
|
|---|
| Topical term or geographic name as entry element |
Computer Science. |
|
|---|
| Topical term or geographic name as entry element |
Artificial Intelligence (incl. Robotics). |
|
|---|
| Topical term or geographic name as entry element |
Data Mining and Knowledge Discovery. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hüllermeier, Eyke. |
| Relator term |
editor. |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Display text |
Printed edition: |
| International Standard Book Number |
9783642141249 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
http://dx.doi.org/10.1007/978-3-642-14125-6 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
E-Book |
