Unverified50% confidenceFactExact time
Expectiles are defined by asymmetrically weighting the squared loss: the solution to minimizing E[w_τ(Y−θ)²] is the τ-expectile, where the weight w_τ is τ when Y>θ and 1−τ otherwise.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe 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)).72% 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).71% similarUnverified损失函数中 tanh(β·E1·E2) 取 β=10,当 β 足够大时将乘积符号信息锐化为接近 ±1 的值69% similarUnverifiedThe mean squared error (MSE) cost function is defined as C(w,b) = 1/(2n) Σ ‖yᵢ − ŷᵢ‖², where each squared error term is always non-negative.67% similarUnverifiedThe 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.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1023449API
curl https://kongchang.com/api/v1/knowledge/claims/1023449MCP
get_claim(id=1023449)