Application of Relief in handwriting recognition
Relief算法在笔迹识别中的应用

Relief and its extensions are feature selection algorithms based on the maximum hypothesis margin principle. They can reduce high dimensionality feature rapidly. Fouce on the handwriting identification task, how the multi-class and unbalance data affect the algorithm process was studied and a new algorithm was given. By assigning the parameters related to the number of examples, the method was appiled in reducing the high dimensionality handwriting features. Experiments indicate that this method not only saved computing time, but also resulted in a substantial improvement in the feature selection.