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DIS
2009
Springer
16 years 1 months ago
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
Yasser El-Manzalawy, Vasant Honavar

Publication
331views
17 years 6 months ago
On Accurate and Efficient Statistical Counting in Sensor-Based Surveillance Systems
Sensor networks have been used in many surveillance systems, providing statistical information about monitored areas. Accurate counting information (e.g., the distribution of the ...
Shuo Guo, Tian He, Mohamed F. Mokbel, John A. Stan...
ICML
2006
IEEE
16 years 7 months ago
Bayesian learning of measurement and structural models
We present a Bayesian search algorithm for learning the structure of latent variable models of continuous variables. We stress the importance of applying search operators designed...
Ricardo Silva, Richard Scheines
FUZZIEEE
2007
IEEE
16 years 24 days ago
Learning Undirected Possibilistic Networks with Conditional Independence Tests
—Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in th...
Christian Borgelt
CVPR
2010
IEEE
15 years 10 months ago
On the design of robust classifiers for computer vision
The design of robust classifiers, which can contend with the noisy and outlier ridden datasets typical of computer vision, is studied. It is argued that such robustness requires l...
Hamed Masnadi-Shirazi, Nuno Vasconcelos, Vijay Mah...