First is that it mixes losses without transfer them into an exponential space, so the formulae for generalized prediction in case of finite number {$K$} of experts is {$g(\omega)= \sum\limits_{k=1}^K \lambda(\omega,\gamma^k)w_k$}, where {$w_k$} are the weights of the experts, {$\lambda$} is a loss function, {$\omega$} are possible events. Second is that it uses a decreasing learning rate {$\eta_t:=\eta / \sqrt{t+1}$} to update the weights of the experts. It is clear, that one can use the same substitution function to provide a prediction. It has an advantage to provide theoretical bounds for weakly mixable functions (bounded functions), and even for unbounded loss functions. The theoretical bound for the loss of Weak Aggregating Algorithm is:
{$L_T \le L_T^k + 2L \sqrt {T \ln K}$}
for all {$T$} and {$k$}, where {$L_T$} is the loss suffered by master over the first {$T$} trials, and {$L_T(k)$} is the loss suffered by the {$k$}-th expert over the first {$T$} trials. The best {$\eta=\sqrt(\ln K)/L$}. The constant $L$ is an upper bound of a loss function: {$\lambda(\omega,\gamma) \le L$}.