This paper introduces a novel method of reducing the number of prototype patterns necessary for accurate recognition of temporal patterns. The nearest neighbor (NN) method is an effective tool in pattern recognition, but the downside is it can be computationally costly when using large quantities of data. To solve this problem, we propose a method of representing the temporal patterns by embedding dynamic time warping (DTW) distance based dissimilarities in vector space. Adaptive boosting (AdaBoost) is then applied for classifier training and feature selection to reduce the number of prototype patterns required for accurate recognition. With a data set of handwritten digits provided by the International Unipen Foundation (iUF), we successfully show that a large quantity of temporal data can be efficiently classified produce similar results to the established NN method while performing at a much smaller cost.