Bernhard Schölkopf is a prominent figure in the field of machine learning, known for his foundational contributions to kernel methods and large-margin classifiers. His work explores the theoretical underpinnings and practical applications of artificial intelligence, focusing on how machines can learn from data in efficient and robust ways. Through his research and influential publications, he has significantly shaped the direction of modern AI, making complex concepts accessible to a wider scientific community.
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.