Regularization, Optimization, Kernels, and Support Vector Machines

Summary

This book is a collection of invited contributions from leading researchers in machine learning. Comprised of 21 chapters, this comprehensive reference covers the latest research and advances in regularization, sparsity, and compressed sensing; describes recent progress in convex and large-scale optimization, kernel methods, and support vector m

Product details

• Authors: Johan A.k. Signoretto Suykens
• Assisted by: Andreas Argyriou (Editor), Argyriou Andreas (Editor), Marco Signoretto (Editor), Signoretto Marco (Editor), Johan A.K. Suykens (Editor)
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Paperback / Softback
• Released: 30.09.2020
• EAN: 9780367658984
• ISBN: 978-0-367-65898-4
• No. of pages: 525
• Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
• Subjects: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; Data Mining, Artificial Intelligence, TECHNOLOGY & ENGINEERING / Electrical, COMPUTERS / Programming / Games, COMPUTERS / Machine Theory, computer science, Electrical Engineering, Automatic control engineering, Communications engineering / telecommunications, Databases, COMPUTERS / Data Science / Data Analytics