The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Efficient crawling through URL ordering
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Intelligent crawling on the World Wide Web with arbitrary predicates
Proceedings of the 10th international conference on World Wide Web
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Using Reinforcement Learning to Spider the Web Efficiently
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Focused Crawling Using Context Graphs
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
PROBABILISTIC MODELS FOR FOCUSED WEB CRAWLING
Computational Intelligence
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With the rapid growth of the World Wide Web, a focused crawling has been increasingly of importance. The goal of the focused crawling is to seek out and collect the pages that are relevant to a predefined set of topics. The determination of the relevance of a page to a specific topic has been addressed as a classification problem. However, when training the classifiers, one can often encounter some difficulties in selecting negative samples. Such difficulties come from the fact that collecting a set of pages relevant to a specific topic is not a classification process by nature. In this paper, we propose a novel focused crawling method using only positive samples to represent a given topic as a form of hyperplane, where we can obtain such representation from a modified Proximal Support Vector Machines. The distance from a page to the hyperplane is used to prioritize the visit order of the page. We demonstrated the performance of the proposed method over the WebKB data set and the Web. The promising results suggest that our proposed method be more effective to the focused crawling problem than the traditional approaches.