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» A general agnostic active learning algorithm
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KDD
2009
ACM
180views Data Mining» more  KDD 2009»
14 years 8 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
WWW
2006
ACM
14 years 8 months ago
Large-scale text categorization by batch mode active learning
Large-scale text categorization is an important research topic for Web data mining. One of the challenges in large-scale text categorization is how to reduce the amount of human e...
Steven C. H. Hoi, Rong Jin, Michael R. Lyu
CSL
2010
Springer
13 years 6 months ago
Voice activity detection based on statistical models and machine learning approaches
The voice activity detectors (VADs) based on statistical models have shown impressive performances especially when fairly precise statistical models are employed. Moreover, the ac...
Jong Won Shin, Joon-Hyuk Chang, Nam Soo Kim
SIGIR
2011
ACM
12 years 10 months ago
Active learning to maximize accuracy vs. effort in interactive information retrieval
We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial documen...
Aibo Tian, Matthew Lease
SIGIR
2008
ACM
13 years 7 months ago
A bayesian logistic regression model for active relevance feedback
Relevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performanc...
Zuobing Xu, Ram Akella