Identical to the standard perceptron proof , e.g. , Collins ( 2002 ) , by inserting in loss-separability for normal separability .
discriminative training methods for hidden markov models theory and experiments with perceptron algorithms
M Collins
Proof . Identical to the standard perceptron proof , e.g. , Collins ( 2002 ) , by inserting in loss-separability for normal separability . Like the original perceptron theorem , this implies that the algorithm will converge .
Identical to the standard perceptron proof , e.g. , @@CITATION , by inserting in loss-separability for normal separability .