1. A method for training the convolutional-neural-network-based classifier, which comprises a plurality of feature map layers, comprising: dividing at least one feature map in at least one of the plurality of the feature map layers into a plurality of regions; performing forward propagation by inputting a training sample with a known flag into the convolutional-neural-network-based classifier to obtain an output result, during the forward propagation, each of the convolutional templates, which correspond to the plurality of regions respectively, is used for obtaining a response value of a neuron in the corresponding region; performing back propagation according to the difference between the output result and the known flag to correct parameters of the convolutional-neural-network-based classifier comprising the weights in the convolutional template; and repeating the above steps until a predetermined condition is met, wherein, one of schemes of the dividing are different for different feature maps and schemes of the dividing are different for different training samples.