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» Learning on the Test Data: Leveraging Unseen Features
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AAAI
2011
12 years 9 months ago
End-User Feature Labeling via Locally Weighted Logistic Regression
Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the lear...
Weng-Keen Wong, Ian Oberst, Shubhomoy Das, Travis ...
AUSDM
2008
Springer
212views Data Mining» more  AUSDM 2008»
13 years 11 months ago
Clustering and Classification of Maintenance Logs using Text Data Mining
Spreadsheets applications allow data to be stored with low development overheads, but also with low data quality. Reporting on data from such sources is difficult using traditiona...
Brett Edwards, Michael Zatorsky, Richi Nayak
ICIP
2001
IEEE
14 years 10 months ago
Image data mining from financial documents based on wavelet features
In this paper, we present a framework for clustering and classifying cheque images according to their payee-line content. The features used in the clustering and classificationpro...
Ossama El Badawy, Mahmoud R. El-Sakka, Khaled Hass...
ICDM
2005
IEEE
148views Data Mining» more  ICDM 2005»
14 years 2 months ago
Hot Item Mining and Summarization from Multiple Auction Web Sites
Online auction Web sites are fast changing, highly dynamic, and complex as they involve tremendous sellers and potential buyers, as well as a huge amount of items listed for biddi...
Tak-Lam Wong, Wai Lam
SIGIR
2008
ACM
13 years 9 months ago
Learning from labeled features using generalized expectation criteria
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
Gregory Druck, Gideon S. Mann, Andrew McCallum