An Improved Incremental Training Algorithm of Support Vector Machines

2011 ◽  
Vol 301-303 ◽  
pp. 677-681
Author(s):  
Liang Qin ◽  
Hong Wei Yin ◽  
Xian Jun Shi ◽  
Zhi Cai Xiao
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Training Algorithm ◽  
Training Samples ◽  
Vector Machines ◽  
Incremental Training

In order to figure out the deficiency of the SVM on extensive sample, nature of SV is studied in this paper. An improved incremental training algorithm is put forward based on dimensional of samples. A chosen gene which got by density and distance criterion is used in this method. In this method the number of training samples is decreased and the space information is keeped. So, the training speed is improved while the precision is not reduced. And the simulation proved the efficiency of this method.

scholarly journals Cross-Validation, Bootstrap, and Support Vector Machines

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Masaaki Tsujitani ◽  
Yusuke Tanaka
Keyword(s):  
Support Vector Machines ◽  
Goodness Of Fit ◽  
Cross Validation ◽  
Prediction Rule ◽  
Support Vector ◽  
Resampling Methods ◽  
Tuning Parameters ◽  
Training Samples ◽  
Vector Machines ◽  
Disease Study

This paper considers the applications of resampling methods to support vector machines (SVMs). We take into account the leaving-one-out cross-validation (CV) when determining the optimum tuning parameters and bootstrapping the deviance in order to summarize the measure of goodness-of-fit in SVMs. The leaving-one-out CV is also adapted in order to provide estimates of the bias of the excess error in a prediction rule constructed with training samples. We analyze the data from a mackerel-egg survey and a liver-disease study.

Soft urban water cover extraction using mixed training samples and Support Vector Machines

2015 ◽  
Vol 36 (13) ◽  
pp. 3331-3344 ◽  
Author(s):  
Xiaoxia Sun ◽  
Liwei Li ◽  
Bing Zhang ◽  
Dongmei Chen ◽  
Lianru Gao
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Urban Water ◽  
Training Samples ◽  
Vector Machines ◽  
Water Cover
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