Support vector machines with adaptive L_q penalty

Keyphrases

• Noise Variables 100%
• L2 Penalty 100%
• Non-adaptive 100%
• Iterative Algorithm 50%
• Feature Selection 50%
• Roughness Penalty 50%
• Application Support 50%
• Hinge Loss Functions 50%
• Learning Methods 50%
• Adaptive Learning 50%
• Adaptive Procedure 50%
• Kaufmann 50%
• Concave Minimization 50%
• 1-norm Support Vector Machines 50%
• Alto 50%
• Multi-class Classification 50%
• L1 Penalty 50%
• Adaptive Approach 50%
• Robustness to Noise 50%
• Real Data Applications 50%
• Learning Procedure 50%

Computer Science

• Application Support 44%
• Multiclass Classification 44%
• Classification Problem 44%
• Called Procedure 44%
• Iterative Algorithm 44%
• Multi Class Classification 44%
• Data Application 44%
• Adaptive Learning 44%

Mathematics

• Classification Problem 44%
• Real Data 44%
• Application Support 44%
• Loss Function 44%
• Minimizes 44%