Adaptive Computation and Machine Learning Ser.: Learning Kernel Classifiers :...

Learning Kernel Classifiers: Theory and Algorithms is a comprehensive textbook by Ralf Herbrich that belongs to the Adaptive Computation and Machine Learning Series published by MIT Press in 2001. The book covers the theory and algorithms of kernel classifiers, making it a valuable resource for computer science and mathematics students. It provides a thorough exploration of the subject area, offering insights into the use of kernel methods for classification tasks. This 384-page hardcover book is a valuable addition to any academic library, offering a deep dive into the world of machine learning and adaptive computation.