Improved Weighted Learning Support Vector Machines (SVM) for High Accuracy

2019 ◽  
Author(s):  
Syahizul Amri Dzulkifli ◽  
Mohd Najib Mohd Salleh ◽  
Kashif Hussain Talpur
Keyword(s):  
Support Vector Machines ◽  
High Accuracy ◽  
Support Vector ◽  
Learning Support ◽  
Vector Machines

scholarly journals Automatic pathology classification using a single feature machine learning support - vector machines

2014 ◽  
Author(s):  
Fernando Yepes-Calderon ◽  
Fabian Pedregosa ◽  
Bertrand Thirion ◽  
Yalin Wang ◽  
Natasha Lepore
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Support Vector ◽  
Learning Support ◽  
Pathology Classification ◽  
Single Feature ◽  
Vector Machines

Active learning support vector machines for optimal sample selection in classification

2004 ◽  
Vol 18 (6) ◽  
pp. 294-305 ◽  
Author(s):  
Simeone Zomer ◽  
Miguel Del Nogal Sánchez ◽  
Richard G. Brereton ◽  
José L. Pérez Pavón
Keyword(s):  
Support Vector Machines ◽  
Active Learning ◽  
Sample Selection ◽  
Support Vector ◽  
Learning Support ◽  
Optimal Sample ◽  
Vector Machines

Applying Support Vector Machines to Predict Tunnel Surrounding Rock Displacement

2010 ◽  
Vol 29-32 ◽  
pp. 1717-1721 ◽  
Author(s):  
Bao Zhen Yao ◽  
Cheng Yong Yang ◽  
Bing Yu ◽  
Fang Fang Jia ◽  
Bo Yu
Keyword(s):  
Support Vector Machines ◽  
High Accuracy ◽  
Surrounding Rock ◽  
Support Vector ◽  
Tunnel Construction ◽  
Displacement Prediction ◽  
Proposed Model ◽  
Vector Machines ◽  
Artificial Neural Network Ann ◽  
Safety Estimation

Displacement prediction of tunnel surrounding rock plays a significant role for safety estimation during tunnel construction. This paper presents an approach to use support vector machines (SVM) to predict tunnel surrounding rock displacement. A stepwise search is also introduced to optimize the parameters in SVM. The data of Fangtianchong tunnel is use to evaluate the proposed model. The comparison between artificial neural network (ANN) and SVM shows that SVM has a high-accuracy prediction than ANN. Results also show SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.

Application of Support Vector Machines to Melissopalynological Data for Honey Classification

2010 ◽  
Vol 1 (2) ◽  
pp. 85-94 ◽  
Author(s):  
Giovanna Aronne ◽  
Veronica De Micco ◽  
Mario R. Guarracino
Keyword(s):  
Support Vector Machines ◽  
Statistical Learning ◽  
Geographical Origin ◽  
High Accuracy ◽  
Support Vector ◽  
Vector Machines ◽  
Innovative Solution ◽  
Z Scores ◽  
Sorrento Peninsula ◽  
Selection Of

In this paper, the authors address the problem of the discrimination of geographical origin and the selection of marker species of honeys using Support Vector Machines and z-scores. The methodology is based on the elaboration of palynological data with statistical learning methodologies. This innovative solution provides a simple yet powerful tool to detect the origin of honey samples. In case of honeys from Sorrento Peninsula, the discrimination from other Italian honeys is obtained with high accuracy.

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