The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning.
- Belief networks
- Fluorescence in situ hybridization (FISH)
- Image classification
- K2 algorithm
- Naive Bayesian classifier