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NIPS
2007
13 years 6 months ago
Predictive Matrix-Variate t Models
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn ...
Shenghuo Zhu, Kai Yu, Yihong Gong
AAAI
2011
12 years 4 months ago
Sparse Matrix-Variate t Process Blockmodels
We consider the problem of modeling network interactions and identifying latent groups of network nodes. This problem is challenging due to the facts i) that the network nodes are...
Zenglin Xu, Feng Yan, Yuan Qi
JMLR
2010
169views more  JMLR 2010»
12 years 11 months ago
Matrix-Variate Dirichlet Process Mixture Models
We are concerned with a multivariate response regression problem where the interest is in considering correlations both across response variates and across response samples. In th...
Zhihua Zhang, Guang Dai, Michael I. Jordan
UMUAI
1998
128views more  UMUAI 1998»
13 years 4 months ago
Using Decision Trees for Agent Modeling: Improving Prediction Performance
A modeling system may be required to predict an agent’s future actions under constraints of inadequate or contradictory relevant historical evidence. This can result in low predi...
Bark Cheung Chiu, Geoffrey I. Webb
NN
2000
Springer
150views Neural Networks» more  NN 2000»
13 years 4 months ago
Multi-step-ahead prediction using dynamic recurrent neural networks
A method for the development of empirical predictive models for complex processes is presented. The models are capable of performing accurate multi-step-ahead (MS) predictions, wh...
Alexander G. Parlos, Omar T. Rais, Amir F. Atiya