研究上指出資料本身的特性會直接影響到分類能力。因此我們設計出一種資料研究的方法，將特徵做最好的應用，提高模式識別的應用，以保證類別分離性。本論文結合類神經網路(NN)於特徵擷取之研究，因此我們提出了NN-SVM的演算法來做為我們分類的工具。在NN-SVM演算法中，利用類神經網路將原始資料，映射為一個新增加的資料集，且於分類之前, 對於任何驗證與測試資料也能做相同的轉換。實驗數據顯示，應用代表性指標資料，分類誤差將會被降低。這結果證實代表性的指標提供給特徵擷取額外有價值的資訊。 Several studies have been reported on the characteristics of data sets which are directly correlated with the capability of the classifier. Therefore, a study in the cognition is conceived, and we suggest the feature optimization to guarantee class separability. We present that the available resource of feature extraction concepts of neural networks(NN) can be applied to the feature optimization problem. Thus, we propose the NN-SVM to set a sufficient number of features compensating for the lack of information. In the NN-SVM algorithm, we use the NN to transform data sets to extract features for support vector machines (SVM) classification. In this way, any validation set and test set subjected to the same transformation before it is classified by the classifier. The experiments on several existing data sets show that, when the augmented data are utilized, the classification errors estimated are reduced by experimental evidence. This implies that the class labels can be used as extra helpful information to feature extraction.