Abstract
Over the past decade, kernel methods have gained much popularity in machine learning. Linear estimators have been popular due to their convenience in analysis and computation. However, nonlinear dependencies exist intrinsically in many real applications and are indispensable for effective modeling. Kernel methods can sometimes offer the best of both aspects. The reproducing kernel Hilbert space provides a convenient way to model nonlinearity, while the estimation is kept linear. Kernels also offer significant flexibility in analyzing generic non-Euclidean objects such as graphs, sets, and dynamic systems. Moreover, kernels induce a rich function space where functional optimization can be performed efficiently. Furthermore, kernels have also been used to define statistical models via exponential families or Gaussian processes and can be factorized by graphical models. Indeed, kernel methods have been widely used in almost all tasks in machine learning.
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Zhang, X. (2017). Kernel Methods. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_144
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_144
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