Prediction of prostate cancer using hair trace element concentration and support vector machine method

2007 ◽  
Vol 116 (3) ◽  
pp. 257-271 ◽  
Author(s):  
JingKang Guo ◽  
Wenhua Deng ◽  
Liecheng Zhang ◽  
Chonghe Li ◽  
Ping Wu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Support Vector Machine ◽  
Trace Element ◽  
Element Concentration ◽  
Trace Element Concentration ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method

Identification of Human Age Using Trace Element Concentrations in Hair and the Support Vector Machine Method

2011 ◽  
Vol 143 (3) ◽  
pp. 1441-1450 ◽  
Author(s):  
Jinmei Lv ◽  
Wuyi Wang ◽  
Fengying Zhang ◽  
Thomas Krafft ◽  
Fuqing Yuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Trace Element ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Element Concentrations

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

scholarly journals Using a support vector machine method to predict the development indices of very high water cut oilfields

2010 ◽  
Vol 7 (3) ◽  
pp. 379-384 ◽  
Author(s):  
Yihua Zhong ◽  
Lei Zhao ◽  
Zhibin Liu ◽  
Yao Xu ◽  
Rong Li
Keyword(s):  
Support Vector Machine ◽  
High Water ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
High Water Cut ◽  
Water Cut ◽  
Very High
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