Least square support vector machine classifier vs a logistic regression classifier on the recognition of numeric digits
Keywords:
Support vector machine, least square, logistic regression, classifier, numeric digits.
Abstract
In this paper is compared the performance of a multi-class least squares support vector machine (LSSVM mc) versus a multi-class logistic regression classifier to problem of recognizing the numeric digits (0-9) handwritten. To develop the comparison was used a data set consisting of 5000 images of handwritten numeric digits (500 images for each number from 0-9), each image of 20 x 20 pixels. The inputs to each of the systems were vectors of 400 dimensions corresponding to each image (not done feature extraction). Both classifiers used OneVsAll strategy to enable multi-classification and a random cross-validation function for the process of minimizing the cost function. The metrics of comparison were precision and training time under the same computational conditions. Both techniques evaluated showed a precision above 95 %, with LS-SVM slightly more accurate. However the computational cost if we found a marked difference: LS-SVM training requires time 16.42 % less than that required by the logistic regression model based on the same low computational conditions.
How to Cite
[1]
D. A. López-Sarmiento, H. C. Manta-Caro, and N. E. Vera-Parra, “Least square support vector machine classifier vs a logistic regression classifier on the recognition of numeric digits”, TecnoL., no. 31, pp. 37–51, Nov. 2011.
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Published
2011-11-30
Issue
Section
Research Papers