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» Learning and Generalization with the Information Bottleneck
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PKDD
2010
Springer
183views Data Mining» more  PKDD 2010»
14 years 8 months ago
Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes
Abstract. In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an i...
Zhao Xu, Kristian Kersting, Thorsten Joachims
WWW
2007
ACM
15 years 10 months ago
Integrating web directories by learning their structures
Documents in the Web are often organized using category trees by information providers (e.g. CNN, BBC) or search engines (e.g. Google, Yahoo!). Such category trees are commonly kn...
Christopher C. Yang, Jianfeng Lin
SIGIR
2006
ACM
15 years 3 months ago
Learning user interaction models for predicting web search result preferences
Evaluating user preferences of web search results is crucial for search engine development, deployment, and maintenance. We present a real-world study of modeling the behavior of ...
Eugene Agichtein, Eric Brill, Susan T. Dumais, Rob...
IJCAI
2003
14 years 11 months ago
Statistics Gathering for Learning from Distributed, Heterogeneous and Autonomous Data Sources
With the growing use of distributed information networks, there is an increasing need for algorithmic and system solutions for data-driven knowledge acquisition using distributed,...
Doina Caragea, Jaime Reinoso, Adrian Silvescu, Vas...
SIGMETRICS
2000
ACM
145views Hardware» more  SIGMETRICS 2000»
14 years 9 months ago
The incremental deployability of RTT-based congestion avoidance for high speed TCP Internet connections
Our research focuses on end-to-end congestion avoidance algorithms that use round trip time (RTT) fluctuations as an indicator of the level of network congestion. The algorithms a...
Jim Martin, Arne A. Nilsson, Injong Rhee