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CVPR
2009
IEEE
16 years 7 months ago
Learning a Distance Metric from Multi-instance Multi-label Data
Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. ...
Rong Jin (Michigan State University), Shijun Wang...
99
Voted
ECCV
2008
Springer
16 years 2 months ago
SIFT Flow: Dense Correspondence across Different Scenes
While image registration has been studied in different areas of computer vision, aligning images depicting different scenes remains a challenging problem, closer to recognition tha...
Ce Liu, Jenny Yuen, Antonio B. Torralba, Josef Siv...
111
Voted
KDD
2006
ACM
145views Data Mining» more  KDD 2006»
16 years 26 days ago
Deriving quantitative models for correlation clusters
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a comm...
Arthur Zimek, Christian Böhm, Elke Achtert, H...
100
Voted
ACMSE
2005
ACM
15 years 6 months ago
The bipartite clique: a topological paradigm for WWWeb user search customization
Web user search customization research has been fueled by the recognition that if the WWW is to attain to its optimal potential as an interactive medium the development of new and...
Brenda F. Miles, Vir V. Phoha
126
Voted
NIPS
2003
15 years 1 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller