The application shows that the algorithms simplify the computing complexity of process neural networks, and raise the efficiency of the network learning and the adaptability to real problem resolving.
应用表明，算法简化了过程神经网络的计算复杂度，提高了网络学习效率和对实际问题求解的适应性。
They also enable the use of multinomial logistic regression algorithm to classify the network flow, and reduce the complexity of the traditional algorithms.
同时利用该组特征仅需采用多项逻辑斯谛回归算法即可实现网络流量的分类，较传统流量分类算法有较低的复杂度。
They also enable the use of multinomial logistic regression algorithm to classify the network flow, and reduce the complexity of the traditional algorithms.
同时利用该组特征仅需采用多项逻辑斯谛回归算法即可实现网络流量的分类，较传统流量分类算法有较低的复杂度。
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