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» Evaluating algorithms that learn from data streams
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VL
2010
IEEE
216views Visual Languages» more  VL 2010»
15 years 5 months ago
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...
IPMI
2009
Springer
16 years 7 months ago
Estimation of Inferential Uncertainty in Assessing Expert Segmentation Performance from STAPLE
The evaluation of the quality of segmentations of an image, and the assessment of intra- and inter-expert variability in segmentation performance, has long been recognized as a dic...
Olivier Commowick, Simon K. Warfield
ECCV
2002
Springer
16 years 8 months ago
A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition
Numerous statistical learning methods have been developed for visual recognition tasks. Few attempts, however, have been made to address theoretical issues, and in particular, stud...
Ming-Hsuan Yang, Dan Roth, Narendra Ahuja
WWW
2009
ACM
16 years 7 months ago
Deriving music theme annotations from user tags
Music theme annotations would be really beneficial for supporting retrieval, but are often neglected by users while annotating. Thus, in order to support users in tagging and to f...
Kerstin Bischoff, Claudiu S. Firan, Raluca Paiu
AUSAI
2004
Springer
15 years 10 months ago
Using Classification to Evaluate the Output of Confidence-Based Association Rule Mining
Abstract. Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for int...
Stefan Mutter, Mark Hall, Eibe Frank