Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.8
Matrix-pattern-oriented Ho-Kashyap classifier has been demonstrated to have a superior classification performance to its vector classifier. However, it is found that the matrixized classifier takes a large computational complexity for convergence in some cases. To overcome the disadvantage, this paper introduces the early stopping technique into the matrixized Ho-Kashyap classifier and presents a Matrix-pattern-oriented Ho-Kashyap classifier with Early Stopping named MatHKES. The presented MatHKES adopts early stopping as a new regularization technique instead of adding a regularization parameter in the criterion. The proposed algorithm achieves: 1) a less running time; 2) a competitive or better classification performance; 3) an avoidance of over fitting in training.
Z. Pan, X. Ni, Z. Wang and S. Chen, "Matrix-Pattern-Oriented Ho-Kashyap Classifier with Early Stopping," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 689-693.