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TRECVID
2008
15 years 2 months ago
Learning TRECVID'08 High-Level Features from YouTube
Run No. Run ID Run Description infMAP (%) training on TV08 data 1 IUPR-TV-M SIFT visual words with maximum entropy 6.1 2 IUPR-TV-MF SIFT with maximum entropy, fused with color+tex...
Adrian Ulges, Christian Schulze, Markus Koch, Thom...
PSD
2004
Springer
108views Database» more  PSD 2004»
15 years 6 months ago
Developing Adoptable Disclosure Protection Techniques: Lessons Learned from a U.S. Experience
The development of new disclosure protection techniques is useful only insofar as those techniques are adopted by statistical agencies. In order for technical experts in disclosure...
Nicholas H. Greenia
152
Voted
ML
2010
ACM
135views Machine Learning» more  ML 2010»
14 years 8 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
PKDD
2010
Springer
128views Data Mining» more  PKDD 2010»
14 years 12 months ago
Learning to Tag from Open Vocabulary Labels
Most approaches to classifying media content assume a fixed, closed vocabulary of labels. In contrast, we advocate machine learning approaches which take advantage of the millions...
Edith Law, Burr Settles, Tom M. Mitchell
ITICSE
2006
ACM
15 years 7 months ago
Students learn CS in different ways: insights from an empirical study
This empirical study demonstrates that students’ learning of computer science takes place in qualitatively different ways. The results consist of categories, where each category...
Anders Berglund, Mattias Wiggberg