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2009 WRI World Congress on Computer Science and Information Engineering
Matrix-Pattern-Oriented Ho-Kashyap Classifier with Early Stopping
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
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.
Citation:
Zhe Wang, Songcan Chen, Zhisong Pan, Xuelei Ni, "Matrix-Pattern-Oriented Ho-Kashyap Classifier with Early Stopping," csie, vol. 3, pp.689-693, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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