scholarly journals Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

2016 ◽  
pp. 3313 ◽  
Author(s):  
Mohammad Aziz ◽  
Mohamed Hussein ◽  
Musa Gabere
Keyword(s):  
Colorectal Cancer ◽  
Support Vector Machines ◽  
Prediction Model ◽  
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.

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.

scholarly journals CROSS ENTROPY UNTUK OPTIMASI LAGRANGE MULTIPLIERS PADA SUPPORT VECTOR MACHINES SEBAGAI MODEL PREDIKSI FINANCIAL DISTRESS

Heuristic ◽  
2016 ◽  
Vol 13 (02) ◽  
Author(s):  
Herlina .
Keyword(s):  
Support Vector Machines ◽  
Prediction Model ◽  
Lagrange Multipliers ◽  
Financial Distress ◽  
Computing Time ◽  
Cross Entropy ◽  
Computational Time ◽  
Support Vector ◽  
Manufacturing Companies ◽  
Vector Machines

The competence in predicting financial distress becomes an important research due tothe advantage in preventing companies financial failure. Besides, financial distressprediction model will give benefit to the investors and creditors. This research developa financial distress prediction model for listed manufacturing companies in Indonesiausing Support Vector Machines (SVM). Mathematically, SVM is formulated in the formof quadratic programming, which requires high computational time in finding theoptimal solution. In this research, Cross Entropy (CE) is used to optimize one of theSVM’s parameter that is Lagrange multipliers to find the optimal solution or nearoptimal solution of dual Lagrange SVM. The accuracy of the prediction model andcomputation time will be compared between standard SVM and CE-SVM. Finally, notethat the CE-SVM can solve classification problems in computing time 9.7 times shorterthan the standard SVM with good accuracy results. Keywords: cross entropy, lagrange multipliers, support vector machines, financialdistress

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