In practice , perceptron-type algorithms are often applied in a batch learning scenario , i.e. , the algorithm is applied for K epochs to a training sample of size T and then used for prediction on an unseen test set ( Freund and Schapire , 1999 ; Collins , 2002 ) .
discriminative training methods for hidden markov models theory and experiments with perceptron algorithms
Michael Collins
Generalization for Online-to-Batch Conversion . In practice , perceptron-type algorithms are often applied in a batch learning scenario , i.e. , the algorithm is applied for K epochs to a training sample of size T and then used for prediction on an unseen test set ( Freund and Schapire , 1999 ; Collins , 2002 ) . The difference to the online learning scenario is that we treat the multi-epoch algorithm as an empirical risk minimizer that selects a final weight vector wT , K whose expected loss on unseen data we would like to bound .
In practice , perceptron-type algorithms are often applied in a batch learning scenario , i.e. , the algorithm is applied for K epochs to a training sample of size T and then used for prediction on an unseen test set ( Freund and Schapire , 1999 ; @@CITATION ) .