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» Predicting labels for dyadic data
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CVPR
2009
IEEE
15 years 5 months ago
What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category ...
Sudheendra Vijayanarasimhan (University of Texas a...
ICCV
2009
IEEE
15 years 2 months ago
Unlabeled data improves word prediction
Labeling image collections is a tedious task, especially when multiple labels have to be chosen for each image. In this paper we introduce a new framework that extends state of ...
Nicolas Loeff, Ali Farhadi, Ian Endres and David A...
COMCOM
2006
105views more  COMCOM 2006»
13 years 10 months ago
Dynamic bandwidth reservation for label switched paths: An on-line predictive approach
Managing the bandwidth allocated to a Label Switched Path in MPLS networks plays a major role for provisioning of Quality of Service and efficient use of resources. In doing so, t...
Tricha Anjali, Carlo Bruni, Daniela Iacoviello, Ca...
KDD
2010
ACM
224views Data Mining» more  KDD 2010»
14 years 1 months ago
Multi-label learning by exploiting label dependency
In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (ex...
Min-Ling Zhang, Kun Zhang
ICPR
2010
IEEE
13 years 7 months ago
The Good, the Bad, and the Ugly: Predicting Aesthetic Image Labels
Automatic classification of the aesthetic content of a picture is one of the challenges in the emerging discipline of computational aesthetics. Any suitable solution must cope wit...
Yaowen Wu, Christian Bauckhage, Christian Thurau