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» Semi-Supervised Active Learning for Sequence Labeling
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ECML
2004
Springer
13 years 11 months ago
Exploiting Unlabeled Data in Content-Based Image Retrieval
Abstract. In this paper, the Ssair (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image ret...
Zhi-Hua Zhou, Ke-Jia Chen, Yuan Jiang
KDD
2006
ACM
180views Data Mining» more  KDD 2006»
14 years 6 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
NAACL
2003
13 years 7 months ago
Active Learning for Classifying Phone Sequences from Unsupervised Phonotactic Models
This paper describes an application of active learning methods to the classification of phone strings recognized using unsupervised phonotactic models. The only training data req...
Shona Douglas
EMNLP
2008
13 years 7 months ago
An Analysis of Active Learning Strategies for Sequence Labeling Tasks
Active learning is well-suited to many problems in natural language processing, where unlabeled data may be abundant but annotation is slow and expensive. This paper aims to shed ...
Burr Settles, Mark Craven
ICMCS
2006
IEEE
125views Multimedia» more  ICMCS 2006»
14 years 8 days 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