Maximum Margin Multi-Label Structured Prediction Supplemental Material
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where π denotes a prior distribution on w, which we set as zero-mean Gaussian, π(w) ∝ exp(− 12 ||w|| ). We choose Qw as a Gaussian centered at αw, Qw(w̄) ∝ exp(− 12 ||w̄ − αw|| ). Then, the KL divergence between Qw and π is just α||w||/2. Analyzing the sample risk L(Qw, S) can be done for each training instance due to i.i.d. sampling. We denote by Ȳ i the predicted output for x with respect to w̄. The proof is complete if we show
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Maximum Margin Multi-Label Structured Prediction
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تاریخ انتشار 2011