MA-STS-Based Social Intimacy Analysis Algorithm Using Real Campus Network Data

Algorithm 2

Training classification model algorithm.

	Input training set D = ;
	learning rate
	process:
(1)	Initialize all connection weights and thresholds in the network randomly in the range (0, 1)
(2)	while
(3)	for all ()do
(4)	The gradient term of neurons in the output layer is calculated according to equation (14)
(5)
(6)	if then
(7)	{}
(8)	i = i−1
(9)	break
(10)	end if
(11)	The gradient term of hidden layer neurons was calculated according to equation (14)
(12)	end for
(13)	until Meet the stop condition
(14)	Output: connection weights with thresholds determined by the neural networks