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Conference International Conference on Case-Based Reasoning (27th : 2019 : Otzenhausen, Germany)
Title Case-based reasoning research and development : 27th international conference, ICCBR 2019, Otzenhausen, Germany, September 8-12, 2019 : proceedings / Kerstin Bach, Cindy Marling (eds.).
Imprint Cham : Springer, [2019]
2019.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Conference International Conference on Case-Based Reasoning (27th : 2019 : Otzenhausen, Germany)
Series Lecture notes in computer science. Lecture notes in artificial intelligence ; 11680.
LNCS sublibrary. SL 7, Artificial intelligence.
Lecture notes in computer science ; http://id.loc.gov/authorities/names/n42015162 11680.
Lecture notes in computer science. Lecture notes in artificial intelligence. http://id.loc.gov/authorities/names/n86736436
LNCS sublibrary. SL 7, Artificial intelligence. http://id.loc.gov/authorities/names/n2008077786
Subject Case-based reasoning -- Congresses.
Alt Name Bach, Kerstin,
Marling, Cindy,
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Conference International Conference on Case-Based Reasoning (27th : 2019 : Otzenhausen, Germany)
Series Lecture notes in computer science. Lecture notes in artificial intelligence ; 11680.
LNCS sublibrary. SL 7, Artificial intelligence.
Lecture notes in computer science ; http://id.loc.gov/authorities/names/n42015162 11680.
Lecture notes in computer science. Lecture notes in artificial intelligence. http://id.loc.gov/authorities/names/n86736436
LNCS sublibrary. SL 7, Artificial intelligence. http://id.loc.gov/authorities/names/n2008077786
Subject Case-based reasoning -- Congresses.
Alt Name Bach, Kerstin,
Marling, Cindy,
Description 1 online resource : illustrations (some color).
polychrome rdacc http://rdaregistry.info/termList/RDAColourContent/1003
Note Online resource; title from PDF title page (SpringerLink, viewed September 16, 2019).
Includes author index.
Contents Intro; Preface; Organization; Abstracts of Invited Papers; Mapping the Challenges and Opportunities of CBR for eXplainable AI; Some Shades of Grey! Interpretability and Explanatory Capacity of Deep Neural Networks; Model-Based Reasoning for Explainable AI as a Service; Contents; Comparing Similarity Learning with Taxonomies and One-Mode Projection in Context of the FEATURE-TAK Framework; 1 Introduction; 2 Weighted One Mode Projection in FEATURE-TAK; 2.1 FEATURE-TAK; 2.2 Integration of the Weighted One-Mode Projection; 3 Evaluation; 3.1 Similarity Matrix Computation and Modelling Assumptions
3.2 Evaluation Results4 Discussion and Outlook; References; An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs; 1 Introduction; 2 Related Work; 3 Explanations Based on Interaction Graphs; 3.1 The Case-Based Explanation System; 3.2 Link Prediction Similarity Measures; 4 Evaluation; 4.1 Data; 4.2 Experimental Setup; 4.3 Results; 5 Conclusions and Future Work; References; Explanation of Recommenders Using Formal Concept Analysis; 1 Introduction; 2 Related Work; 3 Formal Concept Analysis; 4 FCA-Based Explanation Algorithm
4.1 Explanation of the User Profile4.2 Explaining a Recommendation; 5 Evaluation; 5.1 Global Behaviour of the FCA Lattices; 5.2 Item Selection Strategies; 6 Conclusions and Future Work; References; FLEA-CBR -- A Flexible Alternative to the Classic 4R Cycle of Case-Based Reasoning; 1 Introduction; 2 Related Work; 3 FLEA-CBR; 3.1 Problem Description; 3.2 Overview and Background; 3.3 Core Features; 3.4 Find; 3.5 Learn; 3.6 Explain; 3.7 Adapt; 4 Example Usages; 4.1 CBR and Creativity; 4.2 Library Service Optimization; 5 Conclusion and Future Work; References
Lazy Learned Screening for Efficient Recruitment1 Introduction; 2 Related Work; 2.1 Existing Approaches to Screening; 2.2 Existing Semantic Resources; 3 Design and Implementation; 3.1 Case Representation; 3.2 Similarity Functions; 3.3 The CBR Cycle; 4 Test and Evaluation; 4.1 Setup; 4.2 Experiment 1; 4.3 Experiment 2; 5 Results and Discussion; 5.1 Experiment 1; 5.2 Experiment 2; 6 Conclusion and Future Work; References; On the Generalization Capabilities of Sharp Minima in Case-Based Reasoning; 1 Introduction; 2 Background and Related Work
2.1 Case Base Maintenance and Instance-Based Learning2.2 Sharp and Flat Minima of an Error Function; 3 Case Base Maintenance as Optimization Problem; 3.1 Case Base Editing Problem; 3.2 Introspective Problem-Solving Quality; 3.3 Local Optima in Case Base Editing; 3.4 Hill-Climbing Case Base Editors; 4 Sharpness of a Case Base Configuration; 4.1 Characterizing Flat and Sharp Case Base Editing Optima; 4.2 Discussion of the Sharpness Measure; 5 Empirical Evaluation; 5.1 Correlation Between Sharpness and Generalization; 5.2 Hill-Climber Variants and Their Optima; 6 Conclusion; References
Summary This book constitutes the refereed proceedings of the 27th International Conference on Case-Based Reasoning Research and Development, ICCBR 2019, held in Otzenhausen, Germany, in September 2019. The 26 full papers presented in this book were carefully reviewed and selected from 43 submissions. 15 were selected for oral presentation and 11 for poster presentation. The theme of ICCBR 2019, "Explainable AI (XAI)," was highlighted by several activities. These papers, which are included in the proceedings, address many themes related to the theory and application of case-based reasoning and its future direction. -- Provided by publisher.
ISBN 9783030292492 (electronic bk.)
3030292495 (electronic bk.)
9783030292485
ISBN/ISSN 10.1007/978-3-030-29249-2
OCLC # 1119668062


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