TY - GEN
T1 - Iteratively reweighted fitting for reduced multivariate polynomial model
AU - Zuo, Wangmeng
AU - Wang, Kuanquan
AU - Zhang, Dapeng
AU - Yue, Feng
PY - 2007/12/24
Y1 - 2007/12/24
N2 - Recently a class of reduced multivariate polynomial models (RM) has been proposed that performs well in classification tasks involving few features and many training data. The RM method, however, adopts a ridge leastsquare estimator, overlooking the fact that least square error usually does not correspond to minimum classification error. In this paper, we propose an iteratively reweighted regression method and two novel weight functions for fitting the RM model (IRF-RM). The IRF-RM method iteratively increases the weights of samples prone to misclassification and decreases the weights of samples far from the decision boundary, making the IRF-RM model more suitable for efficient pattern classification. A number of benchmark data sets are used to evaluate the IRF-RM method. Experimental results indicate that IRF-RM achieves a higher or comparable classification accuracy compared with RM and several state-of-the-art classification approaches.
AB - Recently a class of reduced multivariate polynomial models (RM) has been proposed that performs well in classification tasks involving few features and many training data. The RM method, however, adopts a ridge leastsquare estimator, overlooking the fact that least square error usually does not correspond to minimum classification error. In this paper, we propose an iteratively reweighted regression method and two novel weight functions for fitting the RM model (IRF-RM). The IRF-RM method iteratively increases the weights of samples prone to misclassification and decreases the weights of samples far from the decision boundary, making the IRF-RM model more suitable for efficient pattern classification. A number of benchmark data sets are used to evaluate the IRF-RM method. Experimental results indicate that IRF-RM achieves a higher or comparable classification accuracy compared with RM and several state-of-the-art classification approaches.
UR - http://www.scopus.com/inward/record.url?scp=37249034235&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
SN - 9783540723929
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 583
EP - 592
BT - Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
T2 - 4th International Symposium on Neural Networks, ISNN 2007
Y2 - 3 June 2007 through 7 June 2007
ER -