Low-DimensionalFeatureLearning withKernelConstruction
نویسندگان
چکیده
Ü Ü • Seeking scoring function as: H(x) = ∑T t=1 αtht(x) • Optimizing pair-wise loss, equivalent to AUC: L = ∑ yi H(xj)]] • Weak rankers h(x): decision stumps and grids (trees of depth 2) • Use weak outputs as new features Φ(x) = (α1h1(x), . . . , αThT (x)) • H can be viewed as linear discrim. rule H(x) = 〈w,Φ(x)〉 for w = 1̄ • T may be too big to store training Φ(xi) + want to exploit unlabeled data⇒ • define kernel as K(x1,x2) = 〈Φ(x1),Φ(x2)〉 ⇒ use “black-box” step
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تاریخ انتشار 2011