On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Web Page Prediction Based on Conditional Random Fields
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Grouped ECOC Conditional Random Fields for Prediction of Web User Behavior
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Web Access Latency Reduction Using CRF-Based Predictive Caching
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
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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 website 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.