In-depth exploration of statistical learning with reproducing kernel Hilbert spaces for advanced AI understanding
Comprehensive analysis of a large class of algorithms, streamlining learning and application
Detailed biomedical applications demonstrate real-world relevance of the theory
Unified theoretical framework simplifies understanding and application of regularization schemes
Suitable for graduate and postgraduate courses in computational mathematics and data science
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This textbook provides an in-depth exploration of statistical learning with reproducing kernels, demonstrating how they can be used to design and justify kernel learning algorithms in artificial intelligence. It also offers two biomedical applications and analyzes a large class of algorithms, making it an ideal resource for graduate and p