Adaptive constrained learning in reproducing kernel hilbert spaces: The robust beamforming case

Keyphrases

• Loss Function 100%
• Reproducing Kernel Hilbert Space 100%
• Constrained Learning 100%
• Robust Beamforming 100%
• Training Data 66%
• Learning Problems 33%
• Numerical Examples 33%
• Regression Techniques 33%
• Cost Function 33%
• Enhanced Resolution 33%
• Infinite Dimension 33%
• Beamforming 33%
• Hilbert Space 33%
• Beamformer 33%
• Closed Convex Sets 33%
• Linear Complexity 33%
• Robust Statistics 33%
• Min-max Optimization 33%
• Robust Constraint 33%
• Strong Convergence 33%
• Infinite Sequences 33%
• Adaptive Learning 33%
• Adaptive Beamforming 33%
• Steering Vector Mismatch 33%
• Beamforming Design 33%
• Optimization Task 33%
• Nonlinear Learning 33%
• Beam Pattern 33%
• Fixed Point Set 33%
• Adaptive Projected Subgradient Method 33%
• Linear Beamforming 33%
• Kernel Regression 33%
• Imposed Constraint 33%

Computer Science

• Reproducing Kernel 100%
• Hilbert Space 100%
• Training Data 50%
• Priori Knowledge 50%
• Unknown Parameter 25%
• Insensitive Loss 25%
• Learning Problem 25%
• Optimization Task 25%
• Final Solution 25%
• Adaptive Learning 25%
• Fixed Points 25%
• Numerical Example 25%
• Mild Condition 25%
• Multiplicity 25%
• Infinite Sequence 25%
• Steering Vector 25%

Mathematics

• Hilbert Space 100%
• Loss Function 75%
• Training Data 50%
• Priori Knowledge 50%
• Minimizes 25%
• Cost Function 25%
• Closed Convex Set 25%
• Linear Complexity 25%
• Strong Convergence 25%
• Infinite Sequence 25%
• Robust Statistic 25%
• Fixed-Point Set 25%
• Min-Max 25%
• Numerical Example 25%
• Subgradient 25%
• Vector Error 25%