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Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
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Neural Computation
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Machine Learning - Special issue on computational learning theory
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Issues in Bayesian analysis of neural network models
Neural Computation
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Bayesian Learning for Neural Networks
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IEEE Intelligent Systems
Theory Refinement of Bayesian Networks with Hidden Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Clustering in Weight Space of Feedforward Nets
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Learning Bayesian Belief Network Classifiers: Algorithms and System
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On the potential of domain literature for clustering and Bayesian network learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian Networks in Ovarian Cancer Diagnosis: Potentials and Limitations
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
Being Bayesian about network structure
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Bayesian error-bars for belief net inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
A characterization of the dirichlet distribution with application to learning Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the sample complexity of learning Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Feature extraction and classification of tumor based on wavelet package and support vector machines
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Artificial Intelligence in Medicine
Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome
Expert Systems with Applications: An International Journal
Combining fuzzy cognitive maps with support vector machines for bladder tumor grading
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
Integration of expert knowledge and image analysis techniques for medical diagnosis
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
Artificial Intelligence in Medicine
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
An Expert Support System for Breast Cancer Diagnosis using Color Wavelet Features
Journal of Medical Systems
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Incorporating prior knowledge into black-box classifiers is still much of an open problem. We propose a hybrid Bayesian methodology that consists in encoding prior knowledge in the form of a (Bayesian) belief network and then using this knowledge to estimate an informative prior for a black-box model (e.g. a multilayer perceptron). Two technical approaches are proposed for the transformation of the belief network into an informative prior. The first one consists in generating samples according to the most probable parameterization of the Bayesian belief network and using them as virtual data together with the real data in the Bayesian learning of a multilayer perceptron. The second approach consists in transforming probability distributions over belief network parameters into distributions over multilayer perceptron parameters. The essential attribute of the hybrid methodology is that it combines prior knowledge and statistical data efficiently when prior knowledge is available and the sample is of small or medium size. Additionally, we describe how the Bayesian approach can provide uncertainty information about the predictions (e.g. for classification with rejection). We demonstrate these techniques on the medical task of predicting the malignancy of ovarian masses and summarize the practical advantages of the Bayesian approach. We compare the learning curves for the hybrid methodology with those of several belief networks and multilayer perceptrons. Furthermore, we report the performance of Bayesian belief networks when they are allowed to exclude hard cases based on various measures of prediction uncertainty.