Subresolution assist feature (SRAF) generation is a very important resolution enhancement technique to improve yield in modern semiconductor manufacturing process. Model-based and rule-based approaches are widely adopted in the semiconductor industry. The model-based SRAF generation can achieve a high accuracy but it is known to be time-consuming and it is hard to obtain consistent SRAFs on the same layout pattern configurations. The rule-based SRAF generation is highly technology dependent and it is becoming extremely difficult to render high-quality results in advanced technology nodes. This paper proposes supervised data learning techniques for fast yet consistent SRAF generation with high-quality results. We first propose the constrained concentric circle with area sampling scheme for feature extraction. Illumination source symmetry-based feature compaction technique is further invented to reduce the training data set size and achieve consistent SRAF predictions. Using accurate model-based SRAFs as training data, classification models based on logistic regression (LGR) and support vector machine are calibrated for SRAF predictions. Moreover, the probability maximum prediction is proposed to generate manufacturing-friendly SRAFs with a greedy simplification scheme. We compare support vector machine and LGR models by embedding into an entire mask optimization flow, where the support vector machine model obtains better lithographic performance. Experimental results demonstrate that, compared with the commercial Calibre tool, supervised data learning techniques for SRAF generation obtain significant speed up (>3X for a 100 um² layout clip) and comparable lithographic performance in terms of edge placement error and process variation band.