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» Using Markov Blankets for Causal Structure Learning
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ICML
2010
IEEE
15 years 4 months ago
Bottom-Up Learning of Markov Network Structure
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...
Jesse Davis, Pedro Domingos
FUIN
2008
108views more  FUIN 2008»
15 years 2 months ago
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such rel...
Wannes Meert, Jan Struyf, Hendrik Blockeel
ICPR
2006
IEEE
16 years 4 months ago
Protein Fold Recognition using a Structural Hidden Markov Model
Protein fold recognition has been the focus of computational biologists for many years. In order to map a protein primary structure to its correct 3D fold, we introduce in this pa...
Djamel Bouchaffra, Jun Tan
134
Voted
PR
2010
147views more  PR 2010»
15 years 1 months ago
Minimum classification error learning for sequential data in the wavelet domain
Wavelet analysis has found widespread use in signal processing and many classification tasks. Nevertheless, its use in dynamic pattern recognition have been much more restricted ...
D. Tomassi, Diego H. Milone, L. Forzani
AAAI
1997
15 years 4 months ago
The "Inverse Hollywood Problem": From Video to Scripts and Storyboards via Causal Analysis
We address the problem of visually detecting causal events and tting them together into a coherent story of the action witnessed by the camera. We show that this can be done by re...
Matthew Brand