Sequence Marked Algorithm

Sequence model is a classical structured model, which has been widly ap-plied in many areas such as Nature Language Processing, Computer Vision and Bioinfomatics. In the past several years, researchers have achieved some results on sequence model studying. But there are still many problems to be explored. In this paper, some problems on sequence model are chosen to conduct in-depth study.First, we propose labelwise margin maximum method to overcome the short-comings of traditional method, which tends to maximum the margin of whole sequence and ignores the correctness of local labels. By using labelwise margin maximum criteria instead of margin maximum criteria in online passive aggressive algorithm, we get a new online algorithm called labelwise PA. Experiments show the new algorithm gets better performance.Secondly, we study the parallel solution of online passive aggressive algo-rithm. We propose parallel PA algorithm by modifying the training process of original online passive aggressive algorithm. We theoretically prove the parallel algorithm has same upper bound of cumulative erros with the original algorithm. We also verify the correctness of the parallel algorithm by experiments, while test-ing the speed ratio of the algorithm on distributed platform. This work provides an effective solution for the use of multi-core computing platform.In this paper, we study on two problems related to the online passive ag-gressive algorithm of sequence labeling. We proposed improvements in the model and algorithm. Experiments show that the improved algorithm obtains better performance.