Advances in Intelligent Data Analysis XXIII 23rd International Symposium on Intelligent Data Analysis, IDA 2025, Konstanz, Germany, May 7–9, 2025, Proceedings /
This volume constitutes the proceedings of the 23rd International Symposium on Intelligent Data Analysis, IDA 2025, which was held in Konstanz, Germany, during May 7–9, 2025. The 35 full papers included in the proceedings were carefully reviewed and selected from 91 submissions. They were organized...
I tiakina i:
| Kaituhi rangatōpū: | |
|---|---|
| Ētahi atu kaituhi: | , , |
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Putanga: | 1st ed. 2025. |
| Rangatū: | Lecture Notes in Computer Science,
15669 |
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1007/978-3-031-91398-3 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-91398-3 | ||
| 003 | DE-He213 | ||
| 005 | 20260706115942.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 250502s2025 sz | s |||| 0|eng d | ||
| 020 | |a 9783031913983 |9 978-3-031-91398-3 | ||
| 024 | 7 | |a 10.1007/978-3-031-91398-3 |2 doi | |
| 050 | 4 | |a QA76.9.D3 | |
| 072 | 7 | |a UN |2 bicssc | |
| 072 | 7 | |a COM021000 |2 bisacsh | |
| 072 | 7 | |a UN |2 thema | |
| 082 | 0 | 4 | |a 005.7 |2 23 |
| 245 | 1 | 0 | |a Advances in Intelligent Data Analysis XXIII |h [electronic resource] : |b 23rd International Symposium on Intelligent Data Analysis, IDA 2025, Konstanz, Germany, May 7–9, 2025, Proceedings / |c edited by Georg Krempl, Kai Puolamäki, Ioanna Miliou. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a XVI, 486 p. 117 illus., 111 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background color |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Lecture Notes in Computer Science, |x 1611-3349 ; |v 15669 | |
| 505 | 0 | |a Applications of Data Science -- Credal Knowledge Tracing for Imprecise and Uncertain MCQ -- Development of Models to Quantify Training Load in Outdoor Running using Inertial Sensors -- Estimating the Learning Capacity of Bacterial Metabolic Networks -- Semi-supervised learning with pairwise instance comparisons for medical instance classification -- Local-global Data Augmentation for Contrastive Learning in Static Sign Language Recognition -- SiamCircle: Trajectory Representation Learning in Free Settings -- Synthetic Tabular Data Detection In the Wild -- Assessing the Impact of Graph Structure Learning in Graph Deviation Networks -- Foundations of Data Science -- The When and How of Target Variable Transformations -- Balancing performance and scalability of demand forecasting ML models -- Balancing global importance and source proximity for personalized recommendations using random walk length -- Counterintuitive Behavior of Clustering Quality: Findings for K-Means on Synthetic and Real Data -- BOWSA: a contribution of sensitivity analysis to improve Bayesian optimization for parameter tuning -- Overfitting in Combined Algorithm Selection and Hyperparameter Optimization -- Local Subgroup Discovery on Attributed Network Graphs -- Imposing Constraints in Probabilistic Circuits via Gradient Optimization -- Natural Language Processing -- Improving Next Tokens via Second-Last Predictions with ’Generate and Refine’ -- Detection of Large Language Model Contamination with Tabular Data -- Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM -- Make Literature-Based Discovery Great Again through Reproducible Pipelines -- Extracting information in a low-resource setting: case study on bioinformatics workflows -- Vocabulary Quality in NLP Datasets: An Autoencoder-Based Framework Across Domains and Languages -- Temporal and Streaming Data Expertise Prediction of Tetris Players Using Eye Tracking Information -- Integrating Inverse and Forward Modeling for Sparse Temporal Data from Sensor Networks -- Bridging Spatial and Temporal Contexts: Sparse Transfer Learning -- Meta-learning and Data Augmentation for Stress Testing Forecasting Models -- Pragmatic Paradigm for Multi-stream Regression -- Two-in-one Models for Event Prediction and Time Series Forecasting. Comparison of Four Deep Learning Approaches to Simulate a Digital Patient under Anesthesia -- An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks -- Performative Drift Resistant Classification using Generative Domain Adversarial Networks -- Explainable and Interpretable Data Science -- Extracting Moore Machines from Transformers using Queries and Counterexamples -- Obtaining Example-Based Explanations from Deep Neural Networks -- Relevance-aware Algorithmic Recourse -- Expanding Polynomial Kernels for Global and Local Explanations of Support Vector Machines -- A Constrained Declarative Based Approach for Explainable Clustering. | |
| 520 | |a This volume constitutes the proceedings of the 23rd International Symposium on Intelligent Data Analysis, IDA 2025, which was held in Konstanz, Germany, during May 7–9, 2025. The 35 full papers included in the proceedings were carefully reviewed and selected from 91 submissions. They were organized in topical sections as follows: Applications of data science, foundations of data science; natural language processing; temporal and streaming data; and explainable and interpretable data science. . | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Education |x Data processing. | |
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 1 | 4 | |a Database Management System. |
| 650 | 2 | 4 | |a Computers and Education. |
| 650 | 2 | 4 | |a Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Natural Language Processing (NLP). |
| 700 | 1 | |a Krempl, Georg. |e editor. |0 (orcid)0000-0002-4153-2594 |1 https://orcid.org/0000-0002-4153-2594 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Puolamäki, Kai. |e editor. |0 (orcid)0000-0003-1819-1047 |1 https://orcid.org/0000-0003-1819-1047 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Miliou, Ioanna. |e editor. |0 (orcid)0000-0002-1357-1967 |1 https://orcid.org/0000-0002-1357-1967 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031913976 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031913990 |
| 830 | 0 | |a Lecture Notes in Computer Science, |x 1611-3349 ; |v 15669 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-91398-3 |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 912 | |a ZDB-2-SXPS | ||
| 912 | |a ZDB-2-LNC | ||
| 912 | |a ZDB-2-242 | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||
| 950 | |a Springer Nature Proceedings Computer Science (SpringerNature-85344) | ||
| 950 | |a Focus Bundle Data, Security and Privacy (SpringerNature-85412) | ||