Machine-Learning Methods on Noisy and Sparse Data

Mathematics

• Sparse Data 100%
• Noisy Data 86%
• Machine Learning 80%
• Cubic Spline 42%
• Training 39%
• Multivariate Adaptive Regression Splines 31%
• Training Samples 24%
• Signal-to-noise Ratio 23%
• Interpolation Method 21%
• Sparsity 19%
• Experimental Data 18%
• Mean square error 18%
• Neural Networks 17%
• Spline 16%
• Deviation 16%
• Prediction 14%
• Experiment 13%
• Restriction 12%
• Unknown 12%
• Simulation 11%
• Framework 10%
• Coefficient 9%
• Model 6%

Engineering & Materials Science

• Splines 71%
• Machine learning 54%
• Deep neural networks 17%
• Mean square error 17%
• Interpolation 15%
• Signal to noise ratio 15%
• Experiments 6%