Abstract Field classi?cation is an extension of the traditional classi?cation framework, by breaking the i.i.d. assumption. In ?eld classi?cation, patterns occur as groups (?elds) of homogeneous styles. By utilizing style consistency, classifying groups of patterns is often more accurate than classifying single patterns. In this paper, we extend the Bayes decision theory, and develop the Field Bayesian Model (FBM) to deal with ?eld classi?cation. Specifically, we propose to learn a Style Normalized Transformation (SNT) for each ?eld. Via the SNTs, the data of different ?elds are transformed to a uniform style space (i.i.d. space). The proposed model is a general and systematic framework, under which many probabilistic models can be easily extended for ?eld classi?cation. To transfer the model to unseen styles, we propose a transductive model called Transfer Bayesian Rule (TBR) based on self-training. We conducted extensive experiments on face, speech and a large-scale handwriting dataset, and got signi?cant error rate reduction compared to the state-of-the-art methods.