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Modified Ho–Kashyap algorithm changes weight calculation step <math>\mathbf{w}(k+1) = \mathbf{Y}^+ \mathbf{b}(k+1)</math> to <math>\mathbf{w}(k+1) = \mathbf{w}(k) + \eta_k \mathbf{Y}^+ |\mathbf{e}(k)|</math>.
Kernel Ho–Kashyap algorithm: Applies [[kernel method]]s (the "[[kernel trick]]") to the Ho–Kashyap framework to enable non-linear classification by implicitly mapping data to a higher-dimensional feature space.<ref>{{cite journal |last=Łęski |first=Jacek |date=2004 |title=Kernel Ho–Kashyap classifier with generalization control. |journal=International Journal of Applied Mathematics and Computer Science |volume=14 |issue=1 |pages=
== See also ==
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