This paper presents a local learning framework in which the local classifiers can be pre-learned and the support size of each classifier can be selected to minimize the error bound. The proposed algorithm is compared with the conventional support vector machine (SVM). Experimental results show that our scheme using the user-defined parameters C and σ is more accurate and less sensitive than the conventional SVM.