scholarly journals A Hybrid Prediction Model Integrating Fuzzy Cognitive Maps with Support Vector Machines

2017 ◽  
Author(s):  
Panayiotis Christodoulou ◽  
Andreas Christoforou ◽  
Andreas S. Andreou
Keyword(s):  
Support Vector Machines ◽  
Prediction Model ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Support Vector ◽  
Hybrid Prediction ◽  
Vector Machines

Combining Fuzzy Cognitive Maps with Support Vector Machines for Bladder Tumor Grading

2006 ◽  
pp. 515-523 ◽  
Author(s):  
Elpiniki Papageorgiou ◽  
George Georgoulas ◽  
Chrysostomos Stylios ◽  
George Nikiforidis ◽  
Peter Groumpos
Keyword(s):  
Support Vector Machines ◽  
Bladder Tumor ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Support Vector ◽  
Tumor Grading ◽  
Vector Machines

scholarly journals LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B3 ◽  
pp. 447-452
Author(s):  
M. Zhou ◽  
C. R. Li ◽  
L. Ma ◽  
H. C. Guan
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Prediction Model ◽  
Image Data ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines ◽  
Waveform Lidar

In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

An application of support vector machines in bankruptcy prediction model

2005 ◽  
Vol 28 (1) ◽  
pp. 127-135 ◽  
Author(s):  
Kyung-Shik Shin ◽  
Taik Soo Lee ◽  
Hyun-jung Kim
Keyword(s):  
Support Vector Machines ◽  
Prediction Model ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Vector Machines

scholarly journals LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B3 ◽  
pp. 447-452 ◽  
Author(s):  
M. Zhou ◽  
C. R. Li ◽  
L. Ma ◽  
H. C. Guan
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Prediction Model ◽  
Image Data ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines ◽  
Waveform Lidar

In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

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