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TIP
2010
155views more  TIP 2010»
13 years 3 months ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He
ICML
2004
IEEE
13 years 10 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
ECML
2007
Springer
13 years 11 months ago
Learning Metrics Between Tree Structured Data: Application to Image Recognition
The problem of learning metrics between structured data (strings, trees or graphs) has been the subject of various recent papers. With regard to the specific case of trees, some a...
Laurent Boyer 0002, Amaury Habrard, Marc Sebban
MIR
2005
ACM
129views Multimedia» more  MIR 2005»
13 years 11 months ago
Multi-graph enabled active learning for multimodal web image retrieval
In this paper, we propose a multimodal Web image retrieval technique based on multi-graph enabled active learning. The main goal is to leverage the heterogeneous data on the Web t...
Xin-Jing Wang, Wei-Ying Ma, Lei Zhang, Xing Li
UAI
2008
13 years 6 months ago
Bayesian Out-Trees
A Bayesian treatment of latent directed graph structure for non-iid data is provided where each child datum is sampled with a directed conditional dependence on a single unknown p...
Tony Jebara