Hadian Rasanan, A.H.Nedaei Janbesaraei, S.Baleanu, D.2025-05-132025-05-1320232364-6837https://doi.org/10.1007/978-981-19-6553-1_3https://hdl.handle.net/20.500.12416/9735Orthogonal functions have many useful properties and can be used for different purposes in machine learning. One of the main applications of the orthogonal functions is producing powerful kernel functions for the support vector machine algorithm. Maybe the simplest orthogonal function that can be used for producing kernel functions is the Chebyshev polynomials. In this chapter, after reviewing some essential properties of Chebyshev polynomials and fractional Chebyshev functions, various Chebyshev kernel functions are presented, and fractional Chebyshev kernel functions are introduced. Finally, the performance of the various Chebyshev kernel functions is illustrated on two sample datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.eninfo:eu-repo/semantics/closedAccessChebyshev PolynomialFractional Chebyshev FunctionsKernel TrickMercer’S TheoremOrthogonal FunctionsBook Part10.1007/978-981-19-6553-1_32-s2.0-85181937072