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CVPR
2006
IEEE
16 years 1 months ago
Semi-Supervised Classification Using Linear Neighborhood Propagation
We consider the general problem of learning from both labeled and unlabeled data. Given a set of data points, only a few of them are labeled, and the remaining points are unlabele...
Fei Wang, Changshui Zhang, Helen C. Shen, Jingdong...
IJCV
2011
264views more  IJCV 2011»
14 years 6 months ago
Cost-Sensitive Active Visual Category Learning
Abstract We present an active learning framework that predicts the tradeoff between the effort and information gain associated with a candidate image annotation, thereby ranking un...
Sudheendra Vijayanarasimhan, Kristen Grauman
PAMI
2008
176views more  PAMI 2008»
14 years 11 months ago
Learning Flexible Features for Conditional Random Fields
Abstract-- Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increas...
Liam Stewart, Xuming He, Richard S. Zemel
EMNLP
2010
14 years 9 months ago
Confidence in Structured-Prediction Using Confidence-Weighted Models
Confidence-Weighted linear classifiers (CW) and its successors were shown to perform well on binary and multiclass NLP problems. In this paper we extend the CW approach for sequen...
Avihai Mejer, Koby Crammer
ICML
2004
IEEE
15 years 5 months ago
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses poin...
Hisashi Kashima, Yuta Tsuboi