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NIPS
2008
14 years 11 months ago
Stochastic Relational Models for Large-scale Dyadic Data using MCMC
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
Shenghuo Zhu, Kai Yu, Yihong Gong
INCDM
2010
Springer
204views Data Mining» more  INCDM 2010»
15 years 1 months ago
Combining Business Process and Data Discovery Techniques for Analyzing and Improving Integrated Care Pathways
Hospitals increasingly use process models for structuring their care processes. Activities performed to patients are logged to a database but these data are rarely used for managin...
Jonas Poelmans, Guido Dedene, Gerda Verheyden, Her...
PAMI
2002
108views more  PAMI 2002»
14 years 10 months ago
Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC)
We propose a method for choosing the number of colors or true gray levels in an image; this allows fully automatic segmentation of images. Our underlying probability model is a hid...
Derek C. Stanford, Adrian E. Raftery
ICCV
1999
IEEE
15 years 2 months ago
Learning Low-Level Vision
We describe a learning-based method for low-level vision problems--estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images, m...
William T. Freeman, Egon C. Pasztor
AAAI
2004
14 years 11 months ago
Analogical Path Planning
We present a probabilistic method for path planning that considers trajectories constrained by both the environment and an ensemble of restrictions or preferences on preferred mot...
Saul Simhon, Gregory Dudek