In supervised learning, one of the major learning methods is memorization learning (ML). Since it reduces only the training error, ML does not guarantee good generalization capability in general. When ML is used, however, acquiring good generalization capability is expected. This usage of ML was interpreted by one of the present authors, H. Ogawa, as a means of realizing 'true objective learning' which directly takes generalization capability into account, and introduced the concept of admissibility. If a learning method can provide the same generalization capability as a true objective learning, it is said that the objective learning admits the learning method. Hence, if admissibility does not hold, making it hold becomes important. In this paper, we introduce the concept of realization of admissibility, and devise a realization method of admissibility of ML with respect to projection learning which directly takes generalization capability into account.