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» Active Mining in a Distributed Setting
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KDD
2010
ACM
247views Data Mining» more  KDD 2010»
13 years 10 months ago
Active learning for biomedical citation screening
Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe ...
Byron C. Wallace, Kevin Small, Carla E. Brodley, T...
SDM
2009
SIAM
117views Data Mining» more  SDM 2009»
14 years 5 months ago
Spatially Cost-Sensitive Active Learning.
In active learning, one attempts to maximize classifier performance for a given number of labeled training points by allowing the active learning algorithm to choose which points...
Alexander Liu, Goo Jun, Joydeep Ghosh
ISPASS
2007
IEEE
14 years 2 months ago
Simplifying Active Memory Clusters by Leveraging Directory Protocol Threads
Address re-mapping techniques in so-called active memory systems have been shown to dramatically increase the performance of applications with poor cache and/or communication beha...
Dhiraj D. Kalamkar, Mainak Chaudhuri, Mark Heinric...
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 9 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
ECAI
2010
Springer
13 years 6 months ago
From bursty patterns to bursty facts: The effectiveness of temporal text mining for news
Many document collections are by nature dynamic, evolving as the topics or events they describe change. The goal of temporal text mining is to discover bursty patterns and to ident...
Ilija Subasic, Bettina Berendt