LEADER 01302nam0 22003131i 450 001 UON00450030 005 20231205105037.444 100 $a20150130d1989 |0itac50 ba 101 $ager 102 $aBE 105 $a|||| ||||| 200 1 $aˆDer ‰codex vindobonensis 418$eseine Vorlage und seine Schreiber$fRudolf Riedinger 210 $aSteenbrugis$cin Abbatia S. Petri ; Dordrecht$cKluwer Academic Publishers$d1989 215 $a108 p., ill.$c60 tav. facsimile$d25 cm. 316 $aFondo Lucentini$5IT-UONSI F.L.0345 410 1$1001UON00449939$12001 $aInstrumenta patristica$v17 606 $aMANOSCRITTI$3UONC025889$2FI 620 $aNL$dDordrecht$3UONL000345 620 $aBE$dSteenbrugge$3UONL005227 676 $a091$cMANOSCRITTI$v21 700 1$aRIEDINGER$bRudolf$3UONV225388$0171251 712 $aAbbatia Sancti Petri$3UONV260639$4650 712 $aKluwer Academic Publishers$3UONV265658$4650 801 $aIT$bSOL$c20240220$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00450030 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI F.L. 0345 $eSI 12435 7 Fondo Lucentini$sBuono 996 $aCodex vindobonensis 418$91329631 997 $aUNIOR LEADER 07178nam 22007455 450 001 9910747595603321 005 20251107151024.0 010 $a3-031-45275-5 024 7 $a10.1007/978-3-031-45275-8 035 $a(MiAaPQ)EBC30775402 035 $a(Au-PeEL)EBL30775402 035 $a(DE-He213)978-3-031-45275-8 035 $a(PPN)272913812 035 $a(CKB)28477901100041 035 $a(EXLCZ)9928477901100041 100 $a20231007d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDiscovery Science $e26th International Conference, DS 2023, Porto, Portugal, October 9?11, 2023, Proceedings /$fedited by Albert Bifet, Ana Carolina Lorena, Rita P. Ribeiro, Joćo Gama, Pedro H. Abreu 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (725 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14276 311 08$aPrint version: Bifet, Albert Discovery Science Cham : Springer,c2023 9783031452741 327 $aEnsembles of classifiers and quantifiers with data fusion for Quantification Learning -- Exploring the Intricacies of Neural Network Optimization -- Exploring the Reduction of Configuration Spaces of Workflows -- iSOUP-SymRF: Symbolic feature ranking with random forests in online multi-target regression -- Knowledge-Guided Additive Modeling For Supervised Regression -- Audience Prediction for Game Streaming Channels Based on Vectorization of User Comments -- From Tweets to Stance: An Unsupervised Framework for User Stance Detection on Twitter -- GLORIA: A Graph Convolutional Network-based Approach for Review Spam Detection -- Unmasking COVID-19 False Information on Twitter: a Topic-based Approach with BERT -- Unsupervised Key-Phrase Extraction from Long Texts with Multilingual Sentence Transformers -- Counterfactuals Explanations for Outliers via Subspaces Density Contrastive Loss -- Explainable Spatio-Temporal Graph Modeling -- ProbabilisticScoring Lists for Interpretable Machine Learning -- Refining Temporal Visualizations Using the Directional Coherence Loss -- Semantic enrichment of explanations of AI models for healthcare -- Text to Time Series Representations: Towards Interpretable Predictive Models -- Enhancing intra-modal similarity in a cross-modal triplet loss -- Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy -- Extrapolation is Not the Same as Interpolation -- Gene Interactions in Survival Data Analysis: A Data-driven Approach Using Restricted Mean Survival Time and Literature Mining -- Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features -- EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories -- Fairness-aware Mixture of Experts with Interpretability Budgets -- GenFair: A Genetic Fairness-Enhancing Data Generation Framework -- Privacy-Preserving Learning of Random Forests Without Revealing the Trees -- Unlearning Spurious Correlations in Chest X-ray Classification -- Explaining the Chronological Attribution of Greek Papyri Images -- Leveraging the Spatiotemporal Analysis of Meisho-e Landscapes -- Predictive Inference Model of the Physical Environment that emulates Predictive Coding -- Transferring a Learned Qualitative Cart-Pole Control Model to Uneven Terrains -- Which Way to Go - Finding Frequent Trajectories Through Clustering -- Boosting-based Construction of BDDs for Linear Threshold Functions and Its Application to Verification of Neural Networks -- Interpretable Data Partitioning through Tree-based Clustering Methods -- Jaccard-constrained dense subgraph discovery -- RIMBO - an ontology for model revision databases -- Unsupervised Graph Neural Networks for Source Code Similarity Detection -- A Universal Approach for Post-Correcting Time Series -- Forecasts: Reducing Long-term ErrorsIn Multistep Scenarios -- Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting -- Pseudo Session-Based Recommendation with Hierarchical Embedding and Session Attributes -- Chance and the predictive limit in basketball (both college and professional) -- Exploring Label Correlations for Quantification of ICD Codes -- LGEM+: a first-order logic framework for automated improvement of metabolic network models through abduction -- Predicting age from human lung tissue through multi-modal data integration -- Error Analysis on Industry Data:Using Weak Segment Detection for Local Model Agnostic Prediction Intervals -- HEART: Heterogeneous Log Anomaly Detection using Robust Transformers -- Multi-Kernel Time Series Outlier Detection -- Toward Streamlining the Evaluation of Novelty Detection in Data Streams. 330 $aThis book constitutes the proceedings of the 26th International Conference on Discovery Science, DS 2023, which took place in Porto, Portugal, in October 2023. The 37 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 133 submissions. They were organized in topical sections as follows: Machine learning methods and applications; natural language processing and social media analysis; interpretability and explainability in AI; data analysis and optimization; fairness, privacy and security in AI; control and spatio-temporal modeling; graph theory and network analysis; time series and forecasting; healthcare and biological data analysis; anomaly, outlier and novelty detection. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14276 606 $aArtificial intelligence 606 $aEducation$xData processing 606 $aData mining 606 $aApplication software 606 $aSocial sciences$xData processing 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aArtificial Intelligence 606 $aComputers and Education 606 $aData Mining and Knowledge Discovery 606 $aComputer and Information Systems Applications 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 615 0$aArtificial intelligence. 615 0$aEducation$xData processing. 615 0$aData mining. 615 0$aApplication software. 615 0$aSocial sciences$xData processing. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 14$aArtificial Intelligence. 615 24$aComputers and Education. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 676 $a617.51 702 $aBifet$b Albert 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910747595603321 996 $aDiscovery Science$92968615 997 $aUNINA