LEADER 03135nam 2200577 450 001 9910143189203321 005 20230607214125.0 010 $a1-280-36629-X 010 $a9786610366293 010 $a0-471-45876-7 010 $a0-471-24968-8 035 $a(CKB)111087027121368 035 $a(EBL)157051 035 $a(OCoLC)304072305 035 $a(SSID)ssj0000118762 035 $a(PQKBManifestationID)11139601 035 $a(PQKBTitleCode)TC0000118762 035 $a(PQKBWorkID)10052055 035 $a(PQKB)10554793 035 $a(MiAaPQ)EBC157051 035 $a(EXLCZ)99111087027121368 100 $a20150106h20022002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCategorical data analysis /$fAlan Agresti 205 $a2nd ed. 210 1$aHoboken, New Jersey :$cWiley-Interscience,$d2002. 210 4$dİ2002 215 $a1 online resource (736 p.) 225 1 $aWiley Series in Probability and Statistics 300 $aDescription based upon print version of record. 311 $a0-471-36093-7 320 $aIncludes bibliographical references and indexes. 327 $aCategorical Data Analysis; Contents; Preface; 1. Introduction: Distributions and Inference for Categorical Data; 2. Describing Contingency Tables; 3. Inference for Contingency Tables; 4. Introduction to Generalized Linear Models; 5. Logistic Regression; 6. Building and Applying Logistic Regression Models; 7. Logit Models for Multinomial Responses; 8. Loglinear Models for Contingency Tables; 9. Building and Extending Loglinear/Logit Models; 10. Models for Matched Pairs; 11. Analyzing Repeated Categorical Response Data 327 $a12. Random Effects: Generalized Linear Mixed Models for Categorical Responses13. Other Mixture Models for Categorical Data*; 14. Asymptotic Theory for Parametric Models; 15. Alternative Estimation Theory for Parametric Models; 16. Historical Tour of Categorical Data Analysis*; Appendix A. Using Computer Software to Analyze Categorical Data; Appendix B. Chi-Squared Distribution Values; References; Examples Index; Author Index; Subject Index 330 $aAmstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Categorical Data Analysis was among those chosen. A valuable new edition of a standard reference ""A 'must-have' book for anyone expecting to do research and/or applications in categorical data analysis.""-Statistics in Medicine on Categorical Data Analysis, First Edition The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in 410 0$aWiley series in probability and statistics. 606 $aMultivariate analysis 615 0$aMultivariate analysis. 676 $a519.5/35 700 $aAgresti$b Alan$0103037 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143189203321 996 $aCategorical data analysis$914689 997 $aUNINA LEADER 08899nam 22008535 450 001 9910983387003321 005 20251229075201.0 010 $a9783031746437 010 $a3031746430 024 7 $a10.1007/978-3-031-74643-7 035 $a(CKB)37122246400041 035 $a(MiAaPQ)EBC31868053 035 $a(Au-PeEL)EBL31868053 035 $a(OCoLC)1482815909 035 $a(BIP)119697584 035 $a(BIP)116596557 035 $a(DE-He213)978-3-031-74643-7 035 $a(EXLCZ)9937122246400041 100 $a20250101d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning and Principles and Practice of Knowledge Discovery in Databases $eInternational Workshops of ECML PKDD 2023, Turin, Italy, September 18?22, 2023, Revised Selected Papers, Part V /$fedited by Rosa Meo, Fabrizio Silvestri 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (495 pages) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v2137 311 08$a9783031746420 311 08$a3031746422 327 $a -- Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications. -- Contextual Data Augmentation for Task-Oriented Dialog Systems. -- Fairness of ChatGPT and the Role Of Explainable-Guided Prompts. -- Deep learning meets Neuromorphic Hardware. -- Non-Dissipative Propagation by Randomized Anti-Symmetric Deep Graph Networks. -- On the Noise Robustness of Analog Complex-Valued Neural Networks. -- Neu-BrAuER: a neuromorphic Braille letters audio-reader for commercial edge devices. -- Discovery challenge. -- Transductive Fire-affected Area Segmentation with False-Color Data. -- Post Wildfire Burnt-up Detection using Siamese UNet. -- Predicting Exoplanetary Features with a Residual Model for Uniform and Gaussian Distributions. -- Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model. -- Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows. -- ITEM: IoT, Edge, and Mobile for Embedded Machine Learning. -- Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification. -- Evaluating custom-precision operator support in MLIR for ARM CPUs. -- microYOLO: Towards Single-Shot Object Detection on Microcontrollers. -- OptiSim: A Hardware-Aware Optimization Space Exploration Tool for CNN Architectures. -- On the Non-Associativity of Analog Computations. -- Quantized dynamics models for hardware-efficient control and planning in model-based RL. -- LIMBO - LearnIng and Mining for BlOckchains. -- Temporal and Geographical Analysis of Real Economic Activities in the Bitcoin Blockchain. -- Machine Learning for Cybersecurity (MLCS 2023). -- A source separation approach to temporal graph modelling for computer networks. -- Quantum Machine Learning for Malware Classification. -- Side-channel Based Intrusion Detection for Network Equipment. -- I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models. -- Concept Drift Detection using Ensemble of Integrally Private Models. -- MIDAS - The 8th Workshop on MIning DAta for financial applicationS. -- ViBERTgrid BiLSTM-CRF: Multimodal Key Information Extraction from Unstructured Financial Documents. -- Comparing Deep RL and Traditional Financial Portfolio Methods - Full paper. -- Occupational Fraud Detection through Agent-based Data Generation. -- Stock Price Time Series Forecasting Using Dynamic Graph Neural Networks and Attention Mechanism in Recurrent Neural Networks. -- Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data. -- Exploring Alternative Data for Nowcasting: A Case Study on US GDP using Topic Attention. -- Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions. -- Boosting Credit Risk Data Quality using Machine Learning and eXplainable AI Techniques. -- Ensemble methods for Stock Market Prediction. -- Workshop on Advancements in Federated Learning. -- Federated Learning with Neural Graphical Models. -- On improving accuracy in Federated Learning using GANs-based pre-training and Ensemble Learning. -- Re-evaluating the Privacy Benefit of Federated Learning. -- Parameterizing Federated Continual Learning for Reproducible Research. 330 $aThe five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023. The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks: Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 ? 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning. 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v2137 606 $aArtificial intelligence 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aComputer engineering 606 $aComputer networks 606 $aApplication software 606 $aData structures (Computer science) 606 $aInformation theory 606 $aEducation$xData processing 606 $aArtificial Intelligence 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aComputer Engineering and Networks 606 $aComputer and Information Systems Applications 606 $aData Structures and Information Theory 606 $aComputers and Education 615 0$aArtificial intelligence. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aApplication software. 615 0$aData structures (Computer science) 615 0$aInformation theory. 615 0$aEducation$xData processing. 615 14$aArtificial Intelligence. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputer Engineering and Networks. 615 24$aComputer and Information Systems Applications. 615 24$aData Structures and Information Theory. 615 24$aComputers and Education. 676 $a006.31 700 $aMeo$b Rosa$01784701 701 $aSilvestri$b Fabrizio$01784702 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983387003321 996 $aMachine Learning and Principles and Practice of Knowledge Discovery in Databases$94316289 997 $aUNINA