Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Error Correcting Output Coding-Based Conditional Random Fields for Web Page Prediction
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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 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
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Reducing the Web access latency perceived by a Web user has become a problem of interest. Web prefetching and caching are two effective techniques that can be used together to reduce the access latency problem on the Internet. Because the success of Web prefetching mainly relies on the prediction accuracy of prediction methods, in this paper we employ a powerful sequential learning model, Conditional Random Fields (CRFs), to improve the Web page prediction accuracy for Web prefetching. We also propose a predictive caching scheme by incorporating CRF-based Web prefetching and caching together to reduce the perceived waiting time of Web users further. We show in our experiments that by using CRF-based Web predictive caching, we can achieve higher cache hit ratio and thus reduce more access latency with less extra transmission cost when compared with the predictive caching methods based on the well known Markov Chain models.