RSC Ensemble

RSC Ensemble (Residual Shrinkage Convolutional Ensemble) is a machine learning technique that combines multiple models to improve accuracy and robustness. Each model in the ensemble learns to recognize patterns in data, and their combined predictions reduce errors and increase reliability. The "residual shrinkage" aspect refers to a process that adjusts model responses, helping to suppress noise and irrelevant information. This approach is especially useful in tasks like image or signal analysis where data may be complex or noisy. Overall, RSC Ensemble leverages the strengths of multiple models with smart adjustments to achieve better, more stable results.