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00000cam a2200000 i 4500 |
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on1293852132 |
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OCoLC |
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20241006213017.0 |
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220127s2022 sz o 101 0 eng d |
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| 019 |
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|a 1292034384
|a 1292067950
|a 1292144858
|a 1292350825
|a 1292426737
|a 1294360855
|a 1296666565
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|q (electronic bk.)
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|a 10.1007/978-3-030-93842-0
|2 doi
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|a (OCoLC)1293852132
|z (OCoLC)1292034384
|z (OCoLC)1292067950
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|b Springer
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|b .B46 2021eb
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|a UYQ
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|a HCDD
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| 111 |
2 |
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|a Benelux Conference on Artificial Intelligence
|n (33rd :
|d 2021 :
|c Esch-sur-Alzette, Luxembourg)
|
| 245 |
1 |
0 |
|a Artificial intelligence and machine learning :
|b 33rd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2021, Esch-sur-Alzette, Luxembourg, November 10-12, 2021, Revised selected papers /
|c Luis A. Leiva, Cédric Pruski, Réka Markovich, Amro Najjar, Christoph Schommer (eds.).
|
| 246 |
3 |
|
|a BNAIC/Benelearn 2021
|
| 264 |
|
1 |
|a Cham, Switzerland :
|b Springer,
|c 2022.
|
| 300 |
|
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|a 1 online resource (x, 255 pages) :
|b illustrations (some color).
|
| 336 |
|
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|a text
|b txt
|2 rdacontent
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| 337 |
|
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a Communications in computer and information science,
|x 1865-0937 ;
|v 1530
|
| 520 |
|
|
|a This book contains a selection of the best papers of the 33rd Benelux Conference on Artificial Intelligence, BNAIC/ BENELEARN 2021, held in Esch-sur-Alzette, Luxembourg, in November 2021. The 14 papers presented in this volume were carefully reviewed and selected from 46 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.
|
| 500 |
|
|
|a Includes author index.
|
| 588 |
0 |
|
|a Online resource; title from PDF title page (SpringerLink, viewed January 27, 2022).
|
| 505 |
0 |
|
|a Intro -- Preface -- Organization -- Contents -- Annotating Data -- Active Learning for Reducing Labeling Effort in Text Classification Tasks -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Active Learning -- 3.2 Model Architecture -- 3.3 Query Functions -- 3.4 Heuristics -- 3.5 Experimental Setup -- 4 Results -- 4.1 Active Learning -- 4.2 Query-Pool Size -- 4.3 Heuristics -- 5 Discussion -- A.1 RET Algorithm Computational Cost Analysis -- A.2 Algorithms -- References -- Refining Weakly-Supervised Free Space Estimation Through Data Augmentation and Recursive Training
|
| 505 |
8 |
|
|a 1 Introduction -- 2 Related Work -- 2.1 Supervised Learning for Segmentation -- 2.2 Weakly-Supervised Semantic Segmentation -- 2.3 Unsupervised and Weakly-Supervised Monocular Free Space Segmentation -- 2.4 Training Strategies for Weakly-Supervised Segmentation -- 3 Methodology -- 3.1 Data Augmentation -- 3.2 Recursive Training -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Network Architectures -- 4.4 Training Procedure -- 4.5 Use of Ground Truth Data -- 5 Results -- 5.1 Fully-Supervised Results -- 5.2 Unsupervised and Weakly-Supervised Baselines
|
| 505 |
8 |
|
|a 5.3 Data Augmentation and Recursive Training -- 5.4 Limits of Recursive Training -- 5.5 Qualitative Results -- 6 Conclusion -- References -- Self-labeling of Fully Mediating Representations by Graph Alignment -- 1 Introduction -- 2 Related Work -- 3 Self-labeling of Fully Mediating Representations -- 3.1 Graph Alignment -- 3.2 Method -- 4 Experiments -- 5 Conclusion -- A Appendix -- A.1 Architecture Summary of Graph Recognition Tool -- A.2 Training Details for Graph Recognition Tool -- A.3 Computational Cost per Rich-Labeling Iteration -- A.4 Examples of Cases Where Graph Alignment Fails
|
| 505 |
8 |
|
|a 3 Proposed Method -- 3.1 Adversarial Domain Adaptation for Object Detection -- 4 Implementation Details -- 5 Evaluation -- 5.1 Datasets -- 5.2 Experiments -- 6 Conclusion -- References -- Explaining Outcomes -- Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities -- Abstract -- 1 Introduction -- 2 Theoretical Background -- 3 Research Method -- 3.1 Use Cases -- 3.2 Data Collection -- 3.3 Data Analysis -- 4 Results -- 4.1 Consumer Credit -- 4.2 Credit Risk Management -- 4.3 Anti-money Laundering (AML) -- 4.4 General -- 5 Discussion and Conclusions
|
| 650 |
|
0 |
|a Artificial intelligence
|v Congresses.
|
| 650 |
|
0 |
|a Machine learning
|v Congresses.
|
| 650 |
|
7 |
|a Artificial intelligence
|2 fast
|
| 650 |
|
7 |
|a Machine learning
|2 fast
|
| 655 |
|
7 |
|a proceedings (reports)
|2 aat
|
| 655 |
|
7 |
|a Conference papers and proceedings
|2 fast
|
| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
|
| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
|
| 700 |
1 |
|
|a Leiva, Luis A.
|e editor.
|0 (orcid)0000-0002-5011-1847
|1 https://orcid.org/0000-0002-5011-1847
|
| 700 |
1 |
|
|a Pruski, Cédric,
|e editor.
|1 https://orcid.org/0000-0002-2103-0431
|
| 700 |
1 |
|
|a Markovich, Réka,
|e editor.
|0 (orcid)0000-0002-2488-2293
|1 https://orcid.org/0000-0002-2488-2293
|
| 700 |
1 |
|
|a Najjar, Amro,
|e editor.
|1 https://orcid.org/0000-0001-7784-6176
|
| 700 |
1 |
|
|a Schommer, Christoph,
|e editor.
|0 (orcid)0000-0002-0308-7637
|1 https://orcid.org/0000-0002-0308-7637
|
| 776 |
0 |
8 |
|c Original
|z 3030938417
|z 9783030938413
|w (OCoLC)1286798603
|
| 830 |
|
0 |
|a Communications in computer and information science ;
|v 1530.
|x 1865-0937
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-93842-0
|y Click for online access
|
| 903 |
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|a SPRING-COMP2022
|
| 994 |
|
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|a 92
|b HCD
|