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» Learning to Predict User Operations for Adaptive Scheduling
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EPIA
2003
Springer
14 years 1 months ago
Adaptation to Drifting Concepts
Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an ex...
Gladys Castillo, João Gama, Pedro Medas
IUI
2006
ACM
14 years 2 months ago
Fewer clicks and less frustration: reducing the cost of reaching the right folder
Helping computer users rapidly locate files in their folder hierarchies has become an important research topic in today’s intelligent user interface design. This paper reports o...
Xinlong Bao, Jonathan L. Herlocker, Thomas G. Diet...
ML
2010
ACM
135views Machine Learning» more  ML 2010»
13 years 3 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
ICMCS
2007
IEEE
97views Multimedia» more  ICMCS 2007»
14 years 2 months ago
Predictive Early Object Shedding in Media Processing Workflows
Media-rich ubiquitous distributed media processing workflow systems continuously sense users’ needs, status, and the context, filter and fuse a multitude of real-time media da...
Lina Peng, K. Selçuk Candan
IUI
2011
ACM
12 years 11 months ago
Predicting and compensating for lexicon access errors
Learning a foreign language is a long, error-prone process, and much of a learner’s time is effectively spent studying vocabulary. Many errors occur because words are only partl...
Lars Yencken, Timothy Baldwin