Hybrid Machine Learning

Hybrid machine learning combines different machine learning techniques, often pairing deep learning models with classical methods, to leverage the strengths of each approach and overcome individual limitations. Current research focuses on applications across diverse fields, including weather forecasting, malware detection, and medical image analysis, employing architectures such as neural networks (e.g., LSTMs, CNNs, RNNs), support vector machines, and ensemble methods. This approach enhances model accuracy, robustness, and interpretability, leading to improved predictions and decision-making in various scientific and practical domains.