Jasni, Mohamad Zain and Abdullah, Embong and Rahayu, Santi Puteri and Juwari, S and Purnami, Santi Wulan (2012) Logistic Regression Methods with Truncated Newton Method. Procedia Engineering, 50. pp. 827836. ISSN 18777058. (Published)

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Abstract
Considering twoclass classification, this paper aims to perform further study on the success of Truncated Newton method in Truncated Regularized Kernel Logistic Regression (TRKLR) and Iterative Reweighted Least Square (TRIRLS) on solving the numerical problem of KLR and RLR. The study was conducted by developing the Newton version of TRKLR and TRIRLS algorithm respectively. They are general classifiers which are termed respectively as proposed Newton TRKLR (NTRKLR) and proposed NTR Regularized Logistic Regression (NTRLR). Instead of using IRLS procedure as used by TRKLR and TRIRLS, the proposed algorithms implement NewtonRaphson method as the outer algorithm of Truncated Newton for KLR and RLR respectively. Since, for KLR and RLR, IRLS is equivalent to NewtonRaphson method, both proposed algorithms can be expected to perform as well as TRKLR and TRIRLS. Moreover, both proposed algorithms are mathematically simpler, because they do not need to restate the NewtonRaphson method as the IRLS procedure before such as in TRKLR and TRIRLS. Hence, they simply can be applied as further explanation to the effectiveness of Truncated Newton method in TRKLR and TRIRLS respectively. Numerical experiment with Image Segmentation data set has demonstrated that proposed NTRKLR performs effectively when exist the singularity and the training time problem in using NewtonRaphson method for KLR (KLRNR). While proposed NTRLR has performed better training time than RLR with NewtonRaphson (RLRNR) method on Letter Image data set. Moreover, both proposed algorithms have showed consistency with the convergence theory and have promising results, i.e. accurate and stable classification, on image data sets respectively.
Item Type:  Article 

Subjects:  Q Science > QA Mathematics > QA75 Electronic computers. Computer science 
Faculty/Division:  Faculty of Computer System And Software Engineering 
Depositing User:  Users 134 not found. 
Date Deposited:  04 Aug 2014 03:26 
Last Modified:  02 May 2018 06:57 
URI:  http://umpir.ump.edu.my/id/eprint/6189 
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