Regularization, Optimization, Kernels, and Support Vector Machines

Explore the cutting-edge of machine learning with Regularization, Optimization, Kernels, and Support Vector Machines by Johan A. K. Suykens. Published by Taylor & Francis Ltd in 2020, this insightful paperback spans 525 pages and brings together contributions from leading experts in the field.

This comprehensive reference is structured into 21 detailed chapters, delving into the latest advancements in regularization, sparsity, and compressed sensing. It also highlights significant progress in convex and large-scale optimization, alongside kernel methods and support vector machines. Whether you are a researcher, practitioner, or student, this book serves as an essential resource for understanding the complexities and innovations driving today's machine learning landscape. Don't miss the opportunity to enhance your knowledge with this authoritative text!