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Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
ISBN: 978-0-7695-3507-4
pp: 549-553
This paper has proposed a new variance-based sorting initial pattern library algorithm for machine learning. First, we sort the training vector set based on vector variance; second, categorize it to several subsets with variance thresholds; last, select some number of pattern vectors from the subsets to form the initial pattern library. This new initial pattern library algorithm is tested by two unsupervised machine learning algorithms: self-organizing feature maps (SOM) algorithm and frequency sensitive self-organizing feature maps (FSOM) algorithm. Experimental results for image coding show that this new initial pattern library algorithm is better than the common random sampling algorithm.
Machine Learning; pattern recognition; self-organizing feature maps; image coding

C. Li, "A New Initial Pattern Library Algorithm for Machine Learning," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 549-553.
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