Data Science, Classification, and Artificial Intelligence for Modeling Decision Making
portes grátis
Data Science, Classification, and Artificial Intelligence for Modeling Decision Making
Chadjipadelis, Theodore; Grane, Aurea; Villalobos, Mario; Trejos, Javier
Springer International Publishing AG
05/2025
190
Mole
9783031858697
Pré-lançamento - envio 15 a 20 dias após a sua edição
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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, E. 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. Gorecki, M.Krzysko, and W. Wolynski: 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. Rodriguez 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. Solis and A. Hernandez: 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.
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Clustering;Classification;Statistical Learning;Machine Learning;Data Analysis;Data Science
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, E. 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. Gorecki, M.Krzysko, and W. Wolynski: 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. Rodriguez 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. Solis and A. Hernandez: 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
