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» Evaluating algorithms that learn from data streams
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SIGIR
2006
ACM
16 years 24 days 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...
TMC
2008
123views more  TMC 2008»
15 years 6 months ago
Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation
In wireless networks, a client's locations can be estimated using signal strength received from signal transmitters. Static fingerprint-based techniques are commonly used for ...
Jie Yin, Qiang Yang, Lionel M. Ni
ICML
2006
IEEE
16 years 7 months ago
Iterative RELIEF for feature weighting
RELIEF is considered one of the most successful algorithms for assessing the quality of features. In this paper, we propose a set of new feature weighting algorithms that perform s...
Yijun Sun, Jian Li
ML
2008
ACM
15 years 6 months ago
A bias/variance decomposition for models using collective inference
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
Jennifer Neville, David Jensen
SOFSEM
2001
Springer
15 years 11 months ago
How Can Computer Science Contribute to Knowledge Discovery?
Knowledge discovery, that is, to analyze a given massive data set and derive or discover some knowledge from it, has been becoming a quite important subject in several fields incl...
Osamu Watanabe