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This chapter includes some conclusions and remarks on the content of the book and on the covered topics. At the same time, a discussion of relevant topics that are not covered by the book is also presented.

Kernel methods, with origins in the pioneering work of Mercer (1909), Bochner (1933), and Aronszajn (1950), have become central tools in modern mathematics and machine learning. This book explores their deep connections with approximation theory, highlighting both classical results and cutting-edge developments. Through clear explanations and illustrative examples, it guides readers from foundational concepts to contemporary applications, including computational methods and real-world problem solving. By bridging theory and practice, the text not only provides a rigorous understanding of kernels but also inspires further exploration and research. Suitable for students, researchers, and practitioners, it invites readers to engage with ongoing advances in this dynamic field and to contribute to its future growth.

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19 - Conclusions

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1 - Introduction

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Kernel-Based Methods

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3 results

19 - Conclusions

Summary

1 - Introduction

Summary

Kernel-Based Methods