소장자료
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| 020 | ▼a9783031157912▼9978-3-031-15791-2▲ | ||
| 024 | 7 | ▼a10.1007/978-3-031-15791-2▼2doi▲ | |
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| 050 | 4 | ▼aTA347.A78▲ | |
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| 245 | 1 | 0 | ▼aKI 2022: Advances in Artificial Intelligence▼h[electronic resource] :▼b45th German Conference on AI, Trier, Germany, September 19–23, 2022, Proceedings /▼cedited by Ralph Bergmann, Lukas Malburg, Stephanie C. Rodermund, Ingo J. Timm.▲ |
| 250 | ▼a1st ed. 2022.▲ | ||
| 264 | 1 | ▼aCham :▼bSpringer International Publishing :▼bImprint: Springer,▼c2022.▲ | |
| 300 | ▼aXXII, 225 p. 56 illus., 49 illus. in color.▼bonline resource.▲ | ||
| 336 | ▼atext▼btxt▼2rdacontent▲ | ||
| 337 | ▼acomputer▼bc▼2rdamedia▲ | ||
| 338 | ▼aonline resource▼bcr▼2rdacarrier▲ | ||
| 347 | ▼atext file▼bPDF▼2rda▲ | ||
| 490 | 1 | ▼aLecture Notes in Artificial Intelligence,▼x2945-9141 ;▼v13404▲ | |
| 505 | 0 | ▼aAn Implementation of Nonmonotonic Reasoning with System W -- Leveraging implicit gaze-based user feedback for interactive machine learning -- The Randomness of Input Data Spaces is an A Priori Predictor for Generalization -- Communicating Safety of Planned Paths via Optimally-Simple Explanations -- Assessing the Accuracy-Explainability-Cost Trade-off on Model Selection for Retail Article Categorization -- Enabling Supervised Machine Learning for SMEs through Data Pooling: A Case Study in the Service Industry -- Unsupervised Alignment of Distributional Word Embeddings. NeuralPDE: Modelling Dynamical Systems from Data -- Deep Neural Networks for Geometric Shape Deformation -- Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling -- Optimal Fixed-Premise Repairs of EL TBoxes -- Health And Habit: an Agent-based Approach -- Knowledge Graph Embeddings with Ontologies: Reification for Representing Arbitrary Relations -- Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning -- HanKA: Enriched Knowledge Used by an Adaptive Cooking Assistant -- Automated Kantian Ethics: A Faithful Implementation and Testing Framework -- PEBAM: A Profile-based Evaluation Method for Bias Assessment on Mixed Datasets.▲ | |
| 520 | ▼aChapter "Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.▲ | ||
| 650 | 0 | ▼aArtificial intelligence.▲ | |
| 650 | 0 | ▼aComputer engineering.▲ | |
| 650 | 0 | ▼aComputer networks .▲ | |
| 650 | 0 | ▼aApplication software.▲ | |
| 650 | 0 | ▼aEducation—Data processing.▲ | |
| 650 | 0 | ▼aComputer science—Mathematics.▲ | |
| 650 | 1 | 4 | ▼aArtificial Intelligence.▲ |
| 650 | 2 | 4 | ▼aComputer Engineering and Networks.▲ |
| 650 | 2 | 4 | ▼aComputer and Information Systems Applications.▲ |
| 650 | 2 | 4 | ▼aComputers and Education.▲ |
| 650 | 2 | 4 | ▼aMathematics of Computing.▲ |
| 700 | 1 | ▼aBergmann, Ralph.▼eeditor.▼0(orcid)0000-0002-5515-7158▼1https://orcid.org/0000-0002-5515-7158▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 700 | 1 | ▼aMalburg, Lukas.▼eeditor.▼0(orcid)0000-0002-6866-0799▼1https://orcid.org/0000-0002-6866-0799▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 700 | 1 | ▼aRodermund, Stephanie C.▼eeditor.▼0(orcid)0000-0002-5168-3564▼1https://orcid.org/0000-0002-5168-3564▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 700 | 1 | ▼aTimm, Ingo J.▼eeditor.▼0(orcid)0000-0002-3369-813X▼1https://orcid.org/0000-0002-3369-813X▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 710 | 2 | ▼aSpringerLink (Online service)▲ | |
| 773 | 0 | ▼tSpringer Nature eBook▲ | |
| 776 | 0 | 8 | ▼iPrinted edition:▼z9783031157905▲ |
| 776 | 0 | 8 | ▼iPrinted edition:▼z9783031157929▲ |
| 830 | 0 | ▼aLecture Notes in Artificial Intelligence,▼x2945-9141 ;▼v13404▲ | |
| 856 | 4 | 0 | ▼uhttps://doi.org/10.1007/978-3-031-15791-2▲ |
KI 2022: Advances in Artificial Intelligence[electronic resource] : 45th German Conference on AI, Trier, Germany, September 19–23, 2022, Proceedings
자료유형
국외eBook
서명/책임사항
KI 2022: Advances in Artificial Intelligence [electronic resource] : 45th German Conference on AI, Trier, Germany, September 19–23, 2022, Proceedings / edited by Ralph Bergmann, Lukas Malburg, Stephanie C. Rodermund, Ingo J. Timm.
판사항
1st ed. 2022.
형태사항
XXII, 225 p. 56 illus., 49 illus. in color. online resource.
총서사항
Lecture Notes in Artificial Intelligence , 2945-9141 ; 13404
Lecture Notes in Artificial Intelligence , 2945-9141 ; 13404
Lecture Notes in Artificial Intelligence , 2945-9141 ; 13404
내용주기
An Implementation of Nonmonotonic Reasoning with System W -- Leveraging implicit gaze-based user feedback for interactive machine learning -- The Randomness of Input Data Spaces is an A Priori Predictor for Generalization -- Communicating Safety of Planned Paths via Optimally-Simple Explanations -- Assessing the Accuracy-Explainability-Cost Trade-off on Model Selection for Retail Article Categorization -- Enabling Supervised Machine Learning for SMEs through Data Pooling: A Case Study in the Service Industry -- Unsupervised Alignment of Distributional Word Embeddings. NeuralPDE: Modelling Dynamical Systems from Data -- Deep Neural Networks for Geometric Shape Deformation -- Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling -- Optimal Fixed-Premise Repairs of EL TBoxes -- Health And Habit: an Agent-based Approach -- Knowledge Graph Embeddings with Ontologies: Reification for Representing Arbitrary Relations -- Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning -- HanKA: Enriched Knowledge Used by an Adaptive Cooking Assistant -- Automated Kantian Ethics: A Faithful Implementation and Testing Framework -- PEBAM: A Profile-based Evaluation Method for Bias Assessment on Mixed Datasets.
요약주기
Chapter "Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
주제
ISBN
9783031157912
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