Cette série explore les domaines de pointe des mathématiques appliquées et computationnelles, présentant des méthodes et algorithmes à la pointe de la technologie. Elle met en évidence l'application croissante des techniques mathématiques dans tous les domaines scientifiques. Conçus pour les étudiants diplômés et les professionnels, les ouvrages offrent des présentations pédagogiques solides. La collection vise à informer et à préparer une nouvelle génération de chercheurs.
This book offers a comprehensive introduction to scattered data approximation theory, making it an ideal resource for graduate students and researchers. It covers essential concepts and methodologies, providing a solid foundation for understanding the subject. The text is designed to be self-contained, ensuring accessibility for those new to the field while also serving as a valuable reference for experienced practitioners.
The book offers a comprehensive guide to the numerical methods used for solving a wide range of integral equations. It covers various techniques and approaches, making it a valuable resource for those looking to understand and apply these mathematical concepts effectively.
Sure to be influential, Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.