Learning Timeline Difference for Text Categorization
Fumiyo Fukumoto, Yoshimi Suzuki · 2015
This paper addresses text categorization problem that training data may derive from a different time period from the test data.We present a learning framework which extends a boosting technique to learn accurate model for timeline adaptation.The results showed that the method was comparable to the current state-of-theart biased-SVM method, especially the method is effective when the creation time period of the test data differs greatly from the training data.