Covers classical and advanced learning methods for pattern recognition to provide a thorough foundation
Discusses both primal and dual solution forms for over- and under-determined systems enabling flexible application
Includes solutions for regression, minimum classification error, maximum receiver operating characteristics, bridge regression, and ensemble learning to stay current with literature advancements
Offers practical exercises with real-world applications to enhance learning and understanding
Focuses on handling systems with overwhelming samples or parameters, addressing modern machine learning challenges
Summarized by Shop
This textbook is a consolidation of learning methods which comes in an analytic form. The covered learning methods include classical and advanced solutions to problems of regression, minimum classification error, maximum receiver operating characteristics, bridge regression, ensemble learning and network learning. Both the primal and dual