In this paper, inspired by the support vector machines for classification and the small sphere and large margin method, the study presents a novel large margin minimal reduced enclosing ball learning machine (LMMREB) for pattern classification to improve the classification performance of gap-tolerant classifiers by constructing a minimal enclosing hypersphere separating data with the maximum margin and minimum enclosing volume in the Mercer induced feature space. The basic idea is to find two optimal minimal reduced enclosing balls by adjusting a reduced factor parameter q such that each of binary classes is enclosed by them respectively and the margin between one class pattern and the reduced enclosing ball is maximized. Thus the idea implements implementing both maximum between-class margin and minimum within-class volume. Experimental results obtained with synthetic and real data show that the proposed algorithms are effective and competitive to other related diagrams.