Ensemble learning
Ensemble learning is a machine learning paradigm that combines multiple models to improve prediction accuracy and robustness.
ensemble methods
PETS (probabilistic ensembles with trajectory sampling)
PETS (Probabilistic Ensembles with Trajectory Sampling) is a technique in machine learning that integrates probabilistic modeling with trajectory prediction.
Model merging (neural networks)
Model merging in neural networks is a technique that combines multiple trained neural network models into a single model. This approach aims to integrate knowledge from different models to improve performance, efficiency, or adaptability.
Gradient boosting
Gradient boosting is a machine learning technique used for regression and classification tasks, known for its predictive accuracy and flexibility.