Pattern Based Method

Pattern-based methods leverage recurring structures in data to improve various tasks, aiming to enhance accuracy, efficiency, and robustness compared to purely learning-based approaches. Current research focuses on applying these methods to diverse domains, including text generation (using frameworks like PatternGPT), outlier detection (with weighted pattern-based algorithms), and visual relation prediction (employing decoupled label learning for improved handling of imbalanced datasets). The significance lies in addressing limitations of traditional machine learning models, such as bias and the inability to incorporate external knowledge, leading to improved performance and interpretability across numerous applications.