01794nam 2200385 n 450 99639165660331620221108100751.0(CKB)4940000000107804(EEBO)2240956810(UnM)99862293(EXLCZ)99494000000010780419921013d1647 uh |engurbn||||a|bb|Propositions for peace and for the King to come up to the Parliament, and the Queene and Prince Charles to come over and bee with His Majestie[electronic resource] And a modell for setling of the kingdome: /concluded by his Excellency Sir Thomas Fairfax, and his Councell of Warre, to be propounded to the Parliament. Subscribed John Rushworth Secretary[London s.n.1647]8 pCaption title.Imprint from Wing.Dated and signed at end: August 1. 1647. Signed by the appointment of his Excellency Sir Thomas Fairfax, and the Councell of Warre. Jo. Rushworth, Secret.Annotation on Thomason copy: "Aug: 12 1647".Reproduction of the original in the British Library.eebo-0018Great BritainHistoryCivil War, 1642-1649PeaceEarly works to 1800Great BritainPolitics and government1642-1649Early works to 1800Fairfax Thomas FairfaxBaron,1612-1671.804819Rushworth John1612?-1690.1001954Cu-RivESCu-RivESCStRLINWaOLNBOOK996391656603316Propositions for peace and for the King to come up to the Parliament, and the Queene and Prince Charles to come over and bee with His Majestie2314277UNISA06776nam 22007215 450 991099968380332120250419130153.03-031-85870-010.1007/978-3-031-85870-3(MiAaPQ)EBC32015006(Au-PeEL)EBL32015006(CKB)38517822300041(DE-He213)978-3-031-85870-3(EXLCZ)993851782230004120250419d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierData Science, Classification, and Artificial Intelligence for Modeling Decision Making /edited by Javier Trejos, Theodore Chadjipadelis, Aurea Grané, Mario Villalobos1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (194 pages)Studies in Classification, Data Analysis, and Knowledge Organization,2198-33213-031-85869-7 Preface -- Acknowledgements -- G. Afriyie, D. Hughes, A. Nettel Aguirre, N. Li, C. H. Lee, L. M. Lix, and T. Sajobi: A Comparison of Multivariate Mixed Models and Generalized Estimation Equations Models for Discrimination in Multivariate Longitudinal Data -- C. Adela Anton and I. Smith: A Multivariate Functional Data Clustering Method Using Parsimonious Cluster Weighted Models -- J. P. Arroyo-Castro and S. W. Chou-Chen: Unsupervised Detection of Anomaly in Public Procurement Processes -- Z. Aouabed, M. Achraf Bouaoune, V. Therrien, M. Bakhtyari, M. Hijri, and V. Makarenkov: Predicting Soil Bacterial and Fungal Communities at Different Taxonomic Levels Using Machine Learning -- V. Bouranta, G. Panagiotidou and T. Chadjipadelis: Candidates, Parties, Issues and the Political Marketing Strategies: A Comparative Analysis on Political Competition in Greece -- J. Cervantes, M. Monge, and D. Sabater: Predicting Air Pollution in Beijing, China Using Chemical, and Climate Variables -- J. Champagne Gareau, É. Beaudry, and V. Makarenkov: Towards Topologically Diverse Probabilistic Planning Benchmarks: Synthetic Domain Generation for Markov Decision Processes -- P. Chaparala and P. Nagabhushan: Symbolic Data Analysis Framework for Recommendation Systems: SDA-RecSys -- E. Costa, I. Papatsouma, and A. Markos: A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data -- M. Farnia and N. Tahiri: A New Metric to Classify B Cell Lineage Tree -- T. Górecki, M.Krzyśko, and W. Wolyński: Applying Classification Methods for Multivariate Functional Data -- K. Moussa Sow and N. Ghazzali: Machine Learning-Based Classification and Prediction to Assess Corrosion Degradation in Mining Pipelines -- G. Nason, D. Salnikov, and M. Cortina-Borja: Modelling Clusters in Network Time Series with an Application to Presidential Elections in the USA -- M. A. Nunez and M. A. Schneider: On the Vapnik-Chervonenkis Dimension and Learnability of the Hurwicz Decision Criterion -- W. Pan and L. Billard: Distributional-based Partitioning with Copulas -- G. Panagiotidou and T. Chadjipadelis: Mapping Electoral Behavior and Political Competition: A Comparative Analytical Framework for Voter Typologies and Political Discourses -- O. Rodríguez Rojas: Riemannian Statistics for Any Type of Data -- A. Roy and F. Montes: Hypothesis Testing of Mean Interval for p-dimensional Interval-valued Data -- M. Solís and A. Hernández: UMAP Projections and the Survival of Empty Space: A Geometric Approach to High-Dimensional Data -- Q. Stier and M. C. Thrun: An Efficient Multicore CPU Implementation of the DatabionicSwarm.This book gathers selected and peer-reviewed contributions presented at the 18th Conference of the International Federation of Classification Societies (IFCS 2024), held in San José, Costa Rica, July 15–19, 2024. Covering a wide range of topics, it describes modern methods and real-world applications in data science, classification, and artificial intelligence related to modeling decision making. Numerous novel techniques and innovative applications are investigated, such as anomaly detection in public procurement processes, multivariate functional data clustering, air pollution prediction, benchmark generation for probabilistic planning, recommendation systems based on symbolic data analysis, and methods for clustering mixed-type data. Advanced statistical concepts are explored, including Vapnik-Chervonenkis dimensionality, Riemannian statistics, hypothesis testing for interval-valued data, and mixed models. Furthermore, machine learning techniques are applied to predict soil bacterial and fungal communities, classify electoral behavior and political competition, and assess corrosion degradation in mining pipelines. The diversity of topics discussed in this collection reflects the ongoing advancement and interdisciplinary nature of statistical and data science research, as well as its application across various fields and sectors. These studies contribute to the development of robust methodologies and efficient computational tools to address complex challenges in the era of big data. The book is intended for researchers and practitioners seeking the latest developments and applications in the field of data science and classification.Studies in Classification, Data Analysis, and Knowledge Organization,2198-3321Machine learningMultivariate analysisInformation visualizationData miningArtificial intelligenceData processingStatistical LearningMultivariate AnalysisData and Information VisualizationMachine LearningData Mining and Knowledge DiscoveryData ScienceMachine learning.Multivariate analysis.Information visualization.Data mining.Artificial intelligenceData processing.Statistical Learning.Multivariate Analysis.Data and Information Visualization.Machine Learning.Data Mining and Knowledge Discovery.Data Science.519.50285Trejos Javier1817246Chadjipadelis Theodore1817247Grané Aurea1817248Villalobos Mario1817249MiAaPQMiAaPQMiAaPQBOOK9910999683803321Data Science, Classification, and Artificial Intelligence for Modeling Decision Making4374827UNINA