Navigating Complexity
Statistical Methods, Data Analysis, and Machine Learning for Actionable Insights
Buch in deiner Nähe kaufen
Beschreibung
This volume gathers selected, peer-reviewed contributions presented at the 19th Conference of the International Federation of Classification Societies (IFCS 2026), held on 14–16 July 2026 in Milan, Italy. Reflecting the volume’s motto, Navigating Complexity – Statistical Methods, Data Analysis, and Machine Learning for Actionable Insights, the papers showcase modern methodologies and real-world applications designed to extract actionable insights from intricate data. The topics span a wide range of areas within statistics, machine learning, and data science, including model-based clustering, Bayesian methods, anomaly detection, and predictive modeling. Novel and tailored models are proposed for complex data types, such as mixed-type, compositional, and functional data. Furthermore, advanced statistical concepts and cutting-edge artificial intelligence techniques are explored, underscoring the interdisciplinary effort required to address complex challenges in today’s data-driven landscape.
Founded in 1985, the International Federation of Classification Societies (IFCS) and its biennial conference are dedicated to promoting mutual communication, cooperation, and the interchange of views among those interested in the scientific principles, numerical methods, and practice of data science, data analysis, and classification.
This volume gathers selected, peer-reviewed contributions presented at the 19th Conference of the International Federation of Classification Societies (IFCS 2026), held on 14–16 July 2026 in Milan, Italy. Reflecting the volume’s motto, Navigating Complexity – Statistical Methods, Data Analysis, and Machine Learning for Actionable Insights, the papers showcase modern methodologies and real-world applications designed to extract actionable insights from intricate data. The topics span a wide range of areas within statistics, machine learning, and data science, including model-based clustering, Bayesian methods, anomaly detection, and predictive modeling. Novel and tailored models are proposed for complex data types, such as mixed-type, compositional, and functional data. Furthermore, advanced statistical concepts and cutting-edge artificial intelligence techniques are explored, underscoring the interdisciplinary effort required to address complex challenges in today’s data-driven landscape.
Founded in 1985, the International Federation of Classification Societies (IFCS) and its biennial conference are dedicated to promoting mutual communication, cooperation, and the interchange of views among those interested in the scientific principles, numerical methods, and practice of data science, data analysis, and classification.
Showcases modern methodologies and real-world applications to extract actionable insights from intricate data Features a wide range of topics in statistics, data analysis, machine learning, classification, and data science Underscores the interdisciplinary effort required to address complex challenges in today’s data-driven landscape
Autor*in
Paula Brito
Themen in »Navigating Complexity«
Clustering Classification Artificial Intelligence Data Science Complex Data Data Analysis Bayesian Inference Model-based Clustering Anomaly Detection Predictive Modeling Compositional Data Functional Data Mixed-type Data