The rule-based bootstrapping introduced by Yarowsky, and its co- training variant by Blum and Mitchell, have met with considerable em- pirical success. Earlier work on the theory of co-training has been only loosely related to empirically useful co-training algorithms. Here we give a new PAC-style bound on generalization error which justifies both the use of confidences -- partial rules and partial labeling of the unlabeled data -- and the use of an agreement-based objective function as sug- gested by Collins and Singer. Our bounds apply to the multiclass case, i.e., where instances are to be assigned one of

The rule-based bootstrapping introduced by Yarowsky, and its cotraining variantby Blum and Mitchell, have met with considerable empirical success. Earlier work on the theory of co-training has been only loosely related to empirically useful co-training algorithms. Here we give a new PACstyle bound on generalization error which justifies both the use of confidences -- partial rules and partial labeling of the unlabeled data -- and the use of an agreement-based objective function as suggested byCollins and Singer. Our bounds apply to the multiclass case, i.e., where instances are to be assigned one of labels for