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» Learning aspect models with partially labeled data
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ICMCS
2006
IEEE
125views Multimedia» more  ICMCS 2006»
14 years 1 months ago
Label Disambiguation and Sequence Modeling for Identifying Human Activities from Wearable Physiological Sensors
Wearable physiological sensors can provide a faithful record of a patient’s physiological states without constant attention of caregivers. A computer program that can infer huma...
Wei-Hao Lin, Alexander G. Hauptmann
EDUTAINMENT
2008
Springer
13 years 9 months ago
Learning Models for the Integration of Adaptive Educational Games in Virtual Learning Environments
There is a trend in Virtual Learning Environments (VLE) towards flexible and adapted learning experiences that modify their contents and behavior to suit the needs of different lea...
Javier Torrente, Pablo Moreno-Ger, Baltasar Fern&a...
ACL
2009
13 years 5 months ago
Distant supervision for relation extraction without labeled data
Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that ...
Mike Mintz, Steven Bills, Rion Snow, Daniel Jurafs...
ACL
2008
13 years 9 months ago
Semi-Supervised Sequential Labeling and Segmentation Using Giga-Word Scale Unlabeled Data
This paper provides evidence that the use of more unlabeled data in semi-supervised learning can improve the performance of Natural Language Processing (NLP) tasks, such as part-o...
Jun Suzuki, Hideki Isozaki
ICCV
2009
IEEE
15 years 12 days ago
A Latent Model of Discriminative Aspect
Recognition using appearance features is confounded by phenomena that cause images of the same object to look different, or images of different objects to look the same. This ma...
Ali Farhadi, Mostafa Kamali Tabrizi, Ian Endres, D...