Unverified50% confidenceFactExact time
In 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.
1
Sources
50%
Confidence
Long-term
Relevance
10/5/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe 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.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% similarUnverifiedAfter each round of training, AdaBoost increases the weights of misclassified samples and decreases the weights of correctly classified samples.65% similarUnverifiedBayesian decision theory selects the action a* that minimizes expected loss, i.e., a* = argmin_a E[L(θ, a)|x], where the expectation is taken with respect to the posterior distribution p(θ|x).63% 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).62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/977408API
curl https://kongchang.com/api/v1/knowledge/claims/977408MCP
get_claim(id=977408)