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» Structured Prediction with Relative Margin
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CHI
2007
ACM
14 years 8 months ago
How it works: a field study of non-technical users interacting with an intelligent system
In order to develop intelligent systems that attain the trust of their users, it is important to understand how users perceive such systems and develop those perceptions over time...
Joe Tullio, Anind K. Dey, Jason Chalecki, James Fo...
SIGMOD
2006
ACM
232views Database» more  SIGMOD 2006»
14 years 8 months ago
To search or to crawl?: towards a query optimizer for text-centric tasks
Text is ubiquitous and, not surprisingly, many important applications rely on textual data for a variety of tasks. As a notable example, information extraction applications derive...
Panagiotis G. Ipeirotis, Eugene Agichtein, Pranay ...
CVPR
2010
IEEE
14 years 4 months ago
Scalable Active Matching
In matching tasks in computer vision, and particularly in real-time tracking from video, there are generally strong priors available on absolute and relative correspondence locati...
Ankur Handa, Margarita Chli, Hauke Strasdat, Andre...
SIGMOD
2009
ACM
172views Database» more  SIGMOD 2009»
14 years 3 months ago
Self-organizing tuple reconstruction in column-stores
Column-stores gained popularity as a promising physical design alternative. Each attribute of a relation is physically stored as a separate column allowing queries to load only th...
Stratos Idreos, Martin L. Kersten, Stefan Manegold
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
14 years 3 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...