Bernhard Schölkopf es una figura destacada en el campo del aprendizaje automático, reconocido por sus contribuciones fundamentales a los métodos de kernel y a los clasificadores de margen amplio. Su trabajo explora los fundamentos teóricos y las aplicaciones prácticas de la inteligencia artificial, centrándose en cómo las máquinas pueden aprender de los datos de manera eficiente y robusta. A través de su investigación y publicaciones influyentes, ha dado forma significativamente a la dirección de la IA moderna, haciendo que los conceptos complejos sean accesibles para una comunidad científica más amplia.
This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever
This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.