On the basis of theoretic convergence analyses of a single-coefficient learning algorithm, a transposition rule is proposed, which is applied to the single-coefficient learning algorithm to gain quick convergence speed in the phase of coefficient learning
On the basis of theoretic convergence analyses of a single-coefficient learning algorithm, a transposition rule is proposed, which is applied to the single-coefficient learning algorithm to gain quick convergence speed in the phase of coefficient learning
最后在单参数学习算法收敛性的分析基础上，提出一种变调整规则的单参数学习算法，加快参数学习速度。