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2009 WRI World Congress on Computer Science and Information Engineering
A New Initial Pattern Library Algorithm for Machine Learning
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
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.
Index Terms:
Machine Learning; pattern recognition; self-organizing feature maps; image coding
Citation:
Chen Li, "A New Initial Pattern Library Algorithm for Machine Learning," csie, vol. 5, pp.549-553, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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