Bernhard Schölkopf è una figura di spicco nel campo dell'apprendimento automatico, rinomato per il suo lavoro fondamentale sui metodi kernel e sui classificatori a margine ampio. La sua ricerca approfondisce gli aspetti teorici e le implementazioni pratiche dell'intelligenza artificiale, indagando su come le macchine possano apprendere dai dati in modo efficiente e affidabile. Attraverso le sue significative pubblicazioni e la sua leadership accademica, ha profondamente influenzato la traiettoria dell'IA contemporanea, rendendo concetti sofisticati comprensibili a un vasto pubblico scientifico.
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.