Least Square Support Vector Machine Classifier vs a Logistic Regression Classifier on the Recognition of Numeric Digits
| Autor: | Danilo A. López-Sarmiento, Héctor C. Manta-Caro, Nelson E. Vera-Parra |
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| Jazyk: | English<br />Spanish; Castilian |
| Rok vydání: | 2013 |
| Předmět: | |
| Zdroj: | TecnoLógicas, Vol 0, Iss 31, Pp 37-51 (2013) |
| Druh dokumentu: | article |
| ISSN: | 0123-7799 2256-5337 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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