Research And Application Of Transfer Learn- Ing Algorithm For Text Classification Based On Clustering

Transfer learning can transfer previously knowledge to a new task, helping the learning of a new task. Using plenty of labeled data to help learning very few data is the task of transfer learning.Instance-based transfer learning assumes that certain parts of the data in the source domain can be reused. Thus, we propose a transfer learning algorithm for text classification based on clustering. This algorithm make use of cluster technology to find the instance that can help the learning of the new data from the auxiliary data. Make experiment on Tancorp V1.0 and proved that the algorithm can improve the learning efficiency of the new data.Spam features are constantly changing, the existing filters can not be well adapted to the changing spam. Re-training filters requires a lot of new spam samples, and these data are often difficult to obtain. And no matter what changes, the basic features of spam will not change, these basic features can be found from the existing spam. In this case, apply the proposed algorithm to spam filter have received good effect.