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SDM
2009
SIAM
117views Data Mining» more  SDM 2009»
14 years 4 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
SEKE
2009
Springer
14 years 1 months ago
Detecting Defects with an Interactive Code Review Tool Based on Visualisation and Machine Learning
Code review is often suggested as a means of improving code quality. Since humans are poor at repetitive tasks, some form of tool support is valuable. To that end we developed a p...
Stefan Axelsson, Dejan Baca, Robert Feldt, Darius ...
MM
2005
ACM
123views Multimedia» more  MM 2005»
14 years 18 days ago
How speech/text alignment benefits web-based learning
This demonstration presents an integrated web-based synchronized scenario for many-to-one cross-media correlations between speech (an EFL, English as Foreign Language, lecture wit...
Sheng-Wei Li, Hao-Tung Lin, Herng-Yow Chen
ETS
2000
IEEE
121views Hardware» more  ETS 2000»
13 years 6 months ago
Increasing Access to Learning With Hybrid Audio-Data Collaboration
Internet enabled hybrid audio-data collaboration delivers high quality audio over telephone lines and data interaction over packet switched Internet connections, thus distributing...
Michael W. Freeman, Lawrence W. Grimes, J. Ray Hol...
TKDE
2010
168views more  TKDE 2010»
13 years 5 months ago
Completely Lazy Learning
—Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not complet...
Eric K. Garcia, Sergey Feldman, Maya R. Gupta, San...