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In the field of machine learning, it is a key issue to learn and represent similarity. This paper focuses on the problem of learning similarity with a multikernel method. Motivated by geometric intuition and computability, similarity between patterns is proposed to be measured by their included angle in a kernel-induced Hilbert space. Having noticed that the cosine of such an included angle can be represented by a normalized kernel, it can be said that the task of learning similarity is equivalent to learning an appropriate normalized kernel. In addition, an error bound is also established for learning similarity with the multikernel method. Based on this bound, a boosting-style algorithm is developed. The preliminary experiments validate the effectiveness of the algorithm for learning similarity.