Unverified50% confidenceFactExact time
The weak learner weight formula in AdaBoost is α_t = (1/2)ln((1-ε_t)/ε_t), where ε_t is the weighted error rate for that round.
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10/5/2026
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Related Claims
UnverifiedIn AdaBoost, correctly classified sample weights are multiplied by e^(-α_t), misclassified sample weights are multiplied by e^(+α_t), and then all weights are normalized.78% similarUnverifiedThe weight update formula in AdaBoost derives from the step-by-step minimization of the exponential loss function L=Σ_i e^(-y_i F(x_i)).71% similarUnverifiedThe weight decay constant λ* introduced in the work is defined as λ* = 1 − αₗᵣ·λ, where αₗᵣ is the learning rate and λ is the original weight decay coefficient.70% similarUnverifiedThe paper proves that the learned generative distribution satisfies a finite-sample error bound in Wasserstein-p distance: W_p ≲ n^(-1/d) + n^(-1/(2p))(log(1/ξ))^(1/(2p)).67% similarUnverifiedUnder the assumption that the true mapping is uniformly random, the paper proves a lower bound on the error rate of E ≥ (M/N)δ*(B/M) + (1-M/N)(1-1/K).65% similar
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