Error Correcting Output Coding-Based Conditional Random Fields for Web Page Prediction

Abstract

Web page prefetching has been used efficiently to reduce the access latency problem of the Internet, its success mainly relies on the accuracy of Web page prediction. As powerful sequential learning models, conditional random fields (CRFs) have been used successfully to improve the Web page prediction accuracy when the total number of unique Web pages is small. However, because the training complexity of CRFs is quadratic to the number of labels, when applied to a Web site with a large number of unique pages, the training of CRFs may become very slow and even intractable. In this paper, we decrease the training time and computational resource requirements of CRFs training by integrating error correcting output coding (ECOC) method. Moreover, since the performance of ECOC-based methods crucially depends on the ECOC code matrix in use, we employ a coding method, search coding, to design the code matrix of good quality.