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Kernel random forest : ウィキペディア英語版 | Kernel random forest
In machine learning, kernel random forests establish the connection between random forests and kernel methods. By slightly modifying their definition, random forests can be rewritten as kernel methods, which are more interpretable and easier to analyze. == History == Leo Breiman was the first person to notice the link between random forest and kernel methods. He pointed out that random forests which are grown using i.i.d random vectors in the tree construction are equivalent to a kernel acting on the true margin. Lin and Jeon established the connection between random forests and adaptive nearest neighbor, implying that random forests can be seen as adaptive kernel estimates. Davies and Ghahramani proposed Random Forest Kernel and show that it can empirically outperform state-of-art kernel methods. Scornet〔 first defined KeRF estimates and gave the explicit link between KeRF estimates and random forest. He also gave explicit expressions for kernels based on centred random forest and uniform random forest, two simplified models of random forest. He named these two KeRFs by Centred KeRF and Uniform KeRF,and proved upper bounds on their rates of consistency.
抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Kernel random forest」の詳細全文を読む
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