An experimental study on the selection of Q-value for the L-GEM

Generalization error is very important in machine learning and pattern classification. However, one can not compute the generalization error for a given problem exactly. Therefore, many research efforts have been put to estimate the generalization error for a given classification problem. The localized generalization error model (L-GEM) is one of the recently proposed analytical generalization error upper bound models. In the L-GEM, an upper bound of generalization error of unseen samples within a Q-neighborhood of training samples is provided. The L-GEM has been widely adopted in many application areas, e.g. image classification, corporate credit risk prediction and construction productivity enhancement in civil engineering. However, the selection of Q value is vital to the success of L-GEM to application problems. In this work, we provide an experimental study on the selection of the Q value and found that Q value equal to half of average of input variances yield a good generalization capability of RBFNN.