Analysis of Kernel Performance in Support Vector Machine Using Seven Features Extraction for Obstacle Detection

Keyphrases

• Support Vector Machine 100%
• Feature Extraction 100%
• Obstacle Detection 100%
• Gray-level Co-occurrence 100%
• Road Condition 100%
• Electric Wheelchair 100%
• Wheelchair Users 100%
• Kernel Classes 100%
• Stair Descent 100%
• Matrix Parameter 50%
• Classification Methods 50%
• Video Data 50%
• Linear Kernel 50%
• Image Data 50%
• Polynomial Kernel 50%
• Support Vector Machine Model 50%
• Kernel Function 50%
• Linear Polynomial 50%
• One Solution 50%
• Autonomous Systems 50%
• Support Vector Machine Classification 50%
• System Expansion 50%
• Gaussian Kernel 50%
• Wheelchair 50%
• Linear Kernel Function 50%
• Video Testing 50%
• Data Testing 50%
• Sigmoid Kernel 50%

Engineering

• Feature Extraction 100%
• Support Vector Machine 100%
• Grey Level 50%
• Road 50%
• Cooccurrence Matrix 50%
• Gaussians 25%
• Classification Method 25%
• Image Data 25%
• Linear Function 25%
• Autonomous System 25%
• Sigmoid Function 25%

Computer Science

• Support Vector Machine 100%
• obstacle detection 100%
• Feature Extraction 100%
• Kernel Function 50%
• Occurrence Matrix 50%
• Classification Method 25%
• Autonomous System 25%
• Linear Polynomial 25%