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Statistical Learning and Data Sciences

Statistical Learning and Data Sciences

Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings
MathematicsScience
Pages
458
Published
2015
Language
English

Synopsis

This book explores the foundational principles and advanced applications of statistical learning and data sciences. It covers essential topics in machine learning and data mining, providing a comprehensive resource for understanding complex data analysis techniques. Readers will find detailed discussions on various statistical methods crucial for modern data-driven fields.

Prose complexity
10/10

Vibe

InformativeIntellectualComplex

Genres

Mathematics Science Technology

Authors

A
Alexander Gammerman

Alexander Gammerman is an author recognized for his contributions to complexity theory and statistical learning. His notable works include "Measures of Complexity" and "Statistical Learning and Data Sciences," which are key texts for understanding data science and its mathematical underpinnings.

H
Harris Papadopoulos

Harris Papadopoulos writes technical texts focused on machine learning and data science. His bibliography includes Artificial Intelligence Applications and Innovations and Statistical Learning and Data Sciences. He also contributed to the AIAI 2012 International Workshops collection.

V
Vladimir Vovk

Vladimir Vovk is a distinguished author specializing in statistical learning, probability, and algorithmic complexity. His notable works include Measures of Complexity, Statistical Learning and Data Sciences, and Algorithmic Learning in a Random World. Vovk also explores game theory in Game-Theoretic Foundations for Probability and Finance.