{"product_id":"data-science-classification-and-artificial-intelligence-for-modeling-decision-making-9783031858697","title":"Data Science Classification and Artificial Intelligence for Modeling Decision Making","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"X-NONE\" style=\"mso-ansi-language: X-NONE;\"\u003ePreface.- 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.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. 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.\u003c\/span\u003e\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195455693143,"sku":"9783031858697","price":123.49,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/data-science-classification-and-artificial-intelligence-for-modeling-decision-making-9783031858697","provider":"Book Curl","version":"1.0","type":"link"}