A Comparison of the Euclidean Distance Metric to a Similarity Metric based on Kolmogorov Complexity in Music Classification
نویسندگان
چکیده
This work 1 studies music classification using the 1-Nearest Neighbor rule comparing the Euclidean distance metric to an information distance metric based on Kolmogorov Complexity. The reason for this comparison is two-fold. First, to understand the music classification task and how similarity measures play a role. Secondly, to contribute to the knowledge regarding effective similarity measures by validating previous work. Here we are testing the hypothesis that the Euclidean distance metric will out-perform a similarity metric based on Kolmogorov Complexity. 1Note that all relevant materials of this project are available at http://www.cs.mcgill.ca/~ethul/pub/course/comp652/
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