Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Real-world applications of Bayesian networks
Communications of the ACM
Communications of the ACM
Simulation
Machine Learning
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
| Hi-index | 0.00 |
In real applications of health data management, it is necessary to make Bayesian network (BN) inferences when evidence is not contained in existing conditional probability tables (CPTs). In this paper, we are to augment the learning function to BN inferences from existing CPTs. Based on the support vector machine (SVM) and sigmoid, we first transform existing CPTs into samples. Then we use transformed samples to train the SVM for finding a maximum likelihood hypothesis, and to fit a sigmoid for mapping outputs of the SVM into probabilities. Further, we give the approximate inference method of BNs with maximum likelihood hypotheses. An applied example and preliminary experiments show the feasibility of our proposed methods.