Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.179
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
Chen Li, "A New Initial Pattern Library Algorithm for Machine Learning", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 549-553, doi:10.1109/CSIE.2009.179