Proxy Cache Replacement Algorithms: A History-Based Approach
World Wide Web
A Data Mining Algorithm for Generalized Web Prefetching
IEEE Transactions on Knowledge and Data Engineering
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Caching in Web memory hierarchies
Proceedings of the 2004 ACM symposium on Applied computing
ICML '06 Proceedings of the 23rd international conference on Machine learning
Analysis of Caching and Replication Strategies for Web Applications
IEEE Internet Computing
Exploring the bounds of web latency reduction from caching and prefetching
USITS'97 Proceedings of the USENIX Symposium on Internet Technologies and Systems on USENIX Symposium on Internet Technologies and Systems
Towards the semantic extraction of digital signatures for librarian image-identification purposes
Journal of the American Society for Information Science and Technology
Journal of Artificial Intelligence Research
Prediction in wireless networks by Markov Chains
IEEE Wireless Communications
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In the proposed method for Web page-ranking, a novel theoretic model is introduced and tested by examples of order relationships among IP addresses. The goal is, through a self-organizing procedure, to learn from these examples a real-valued ranking function that induces ranking via a convexity feature. We consider the problem of self-organizing learning from IP data to be represented by a semi-random convex polygon procedure, in which the vertices correspond to IP addresses. Taking into account recent developments in our regularization theory for convex polygons and corresponding Euclidean distance based methods for classification, we develop an algorithmic framework for learning ranking functions based on a Computational Geometric Theory. We provide generalization guarantee for our algorithm, given our recent results, and experimental verification of the potential advantages of our framework.