Abstract
This chapter is not aimed at replacing literature on introduction to kernel methods or Fisher kernels. There are some excellent text books and tutorials on the topic by Schölkopf and Smola (Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press (2002), [1]), Shawe-Taylor, Cristianini (Kernel methods for pattern analysis. Cambridge University Press (2004), [2]), Kung (Kernel methods and machine learning. Cambridge University Press, Princeton University (2014), [3])). In contrast to formal theory and proofs, this chapter briefly describes the evolution of kernel methods and the heuristics and methods that have helped kernel methods evolve over the past many years for solving the challenges faced by current machine learning practitioners and applied scientists.
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Azim, T., Ahmed, S. (2018). Kernel Based Learning: A Pragmatic Approach in the Face of New Challenges. In: Composing Fisher Kernels from Deep Neural Models. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-98524-4_1
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