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ICML
2004
IEEE
14 years 8 months ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
BMVC
2001
13 years 10 months ago
Classifying Surveillance Events from Attributes and Behaviour
In order to develop a high-level description of events unfolding in a typical surveillance scenario, each successfully tracked event must be classified into type and behaviour. I...
Paolo Remagnino, Graeme A. Jones
SIGIR
2008
ACM
13 years 7 months ago
Posterior probabilistic clustering using NMF
We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes th...
Chris H. Q. Ding, Tao Li, Dijun Luo, Wei Peng
ICPR
2004
IEEE
14 years 8 months ago
Probabilistic Combination of Multiple Modalities to Detect Interest
This paper describes a new approach to combine multiple modalities and applies it to the problem of affect recognition. The problem is posed as a combination of classifiers in a p...
Ashish Kapoor, Rosalind W. Picard, Yuri Ivanov
ICCV
2005
IEEE
14 years 9 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu