Numerous theories and algorithms have been developed to solve vectorial data learning problems by searching for the hypothesis that best fits the observed training sample. However, many real-world applications involve samples that are not described as feature vectors, but as (dis)similarity data. Converting vectorial data into (dis)similarity data is more easily performed than converting (dis)similarity data into vectorial data. This study proposes a stochastic iterative distance transformation model for similarity-based learning. The proposed model can be used to identify a clear class boundary in data by modifying the (dis)similarities between examples. The experimental results indicate that the performance of the proposed method is comparable with those of various vector-based and proximity-based learning algorithms.