Fast Likelihood Ratio Optimization Based Upon Genaralized Logarithm and Its Applications

学习算法的似然优化通过使用 alpha 对数进行了推广。这种概括导致比传统方法更快的收敛。选择隐马尔可夫模型估计和独立成分分析算法，因为它们具有很高的影响。 α-对数的使用表现为通过动量项利用过去的信息。这一特性使得收敛速度比传统方法更快。

Likelihood optimization for learning algorithms was generalized by using the alpha-logarithm. This generalization led to a faster convergence than traditional methods. Algorithms on hidden Markov model estimation and independent component analysis were chosen since they have high ramifications. The use of the alpha-logarithm appears as the utilization of past information via momentum terms. This property enabled faster convergence than traditional methods.