Online classifiers for Chinese text classification and filtering

In text classification systems, the quality and quantity of the training document is one of the most import factors which affect performance. But gathering, filtering and classifying the training documents is very difficult. This paper employs feedback mechanism for text classification systems and reduces the need for labeled training documents by unifying the strengths of k-NN and linear classifiers. It collects various types of example documents provided by the users and set up the user profiles. While the documents from stream are matched with the profiles, the relevant documents are used to improve the user profile. The online approach offers the advantage of continuous learning in the batch-adaptive text considerations, on the batch-adaptive filtering task.