scholarly journals A Solving Algorithm of Fuzzy Support Vector Machines Based on Determination of Membership

2012 ◽  
Author(s):  
Hua Duan ◽  
Yanmei Hou
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines ◽  
Fuzzy Support Vector Machines

A Solving Algorithm of Fuzzy Support Vector Machines Based on Determination of Membership

2013 ◽  
Vol 756-759 ◽  
pp. 3399-3403
Author(s):  
Hua Duan ◽  
Yan Mei Hou
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Construction Algorithm ◽  
Vector Machines ◽  
Fuzzy Support Vector Machines ◽  
Smo Algorithm

In order to overcome the issues that Support Vector Machine is sensitive to the outlier and noise points, Fuzzy Support Vector Machine (FSVM) is proposed. The key issue to solve the FSVM is determinate the fuzzy membership. This paper gives an overview of construction algorithm of the fuzzy membership. We also give an algorithm to solve FSVM that is derived from improved-SMO algorithm.

scholarly journals Classification of the Priority of Auditing XBRL Instance Documents with Fuzzy Support Vector Machines Algorithm

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Guang-Yih Sheu
Keyword(s):  
Support Vector Machines ◽  
Financial Distress ◽  
Fuzzy Variable ◽  
Financial Ratios ◽  
Misclassification Rate ◽  
Benford’S Law ◽  
Support Vector ◽  
Vector Machines ◽  
Benford's Law ◽  
Fuzzy Support Vector Machines

Concluding the conformity of XBRL (eXtensible Business Reporting Language) instance documents law to the Benford's law yields apparently different results before and after a company's financial distress. These results bring an idea of finding fraudulent documents from the inspection of financial ratios since the unacceptable conformity implies a large likelihood of a fraudulent document. Fuzzy support vector machines models are developed to implement such an idea. The dependent variable is a fuzzy variable quantifying the conformity of an XBRL instance document to the Benford's law; whereas, independent variables are financial ratios. Nevertheless, insufficient data are available to define any membership function for describing the fuzziness in independent and dependent variables, but the interval factor method is introduced to express that fuzziness. Using the resulting fuzzy support vector machines model, it is suggested that the price-to-book ratio versus equity ratio may be used to classify the priority of auditing XBRL instance documents. The misclassification rate is less than 30 \%. In conclusion, a new and promising application of fuzzy support vector machines algorithm has been found in this study.

scholarly journals Rapid and accurate determination of tissue optical properties using least-squares support vector machines

2011 ◽  
Vol 2 (3) ◽  
pp. 592 ◽  
Author(s):  
Ishan Barman ◽  
Narahara Chari Dingari ◽  
Narasimhan Rajaram ◽  
James W. Tunnell ◽  
Ramachandra R. Dasari ◽  
...  
Keyword(s):  
Optical Properties ◽  
Support Vector Machines ◽  
Least Squares ◽  
Accurate Determination ◽  
Support Vector ◽  
Tissue Optical Properties ◽  
Vector Machines
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