Abstract
Proposed is GLLL2, a hybrid architecture of a global and a local learning module, which learns default and exceptional knowledge respectively from noisy examples. The global learning module, which is a feedforward neural network, captures global trends gradually, while the local learning module stores local exceptions quickly. The latter module distinguishes noise from exceptions, and learns only exceptions, which makes GLLL2 noise-tolerant. The results of experiments show the process in which training examples are formed into default and exceptional knowledge, and demonstrate that the predictive accuracy, the space efficiency, and the training efficiency of GLLL2 is higher than those of each individual module.
