Sample selection based on k-l divergence for effectively training SVM

Computer Science

• Support Vector Machine 100%
• Large Data Set 50%
• Support Vector 50%
• Training Sample 50%
• Selection Method 25%
• Time Complexity 25%
• Computational Complexity 25%
• Hyperplanes 25%
• Space Complexity 25%
• Experimental Result 25%
• Computational Space 25%
• Computational Time 25%

Chemical Engineering

• Support Vector Machine 100%

Engineering

• Hyperplanes 25%
• Computational Space 25%