Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Structure and parameter learning for causal independence and causal interaction models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Unsupervised Bayesian visualization of high-dimensional data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An Unsupervised Bayesian Distance Measure
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Possibilistic causality consistency problem based on asymmetrically-valued causal model
Fuzzy Sets and Systems - Possibility theory and fuzzy logic
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
When discriminative learning of Bayesian network parameters is easy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On supervised selection of Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks with restricted causal interactions
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Dependency networks for collaborative filtering and data visualization
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Classifier learning with supervised marginal likelihood
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
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When performing regression or classification, we are interested in the conditional probability distribution for an outcome or class variable Y given a set of explanatory or input variables X. We consider Bayesian models for this task. In particular, we examine a special class of models, which we call Bayesian regression/classification (BRC) models, that can be factored into independent conditional (y[x) and input (x) models. These models are convenient, because the conditional model (the portion of the full model that we care about) can be analyzed by itself. We examine the practice of transforming arbitrary Bayesian models to BRC models, and argue that this practice is often inappropriate because it ignores prior knowledge that may be important for learning. In addition, we examine Bayesian methods for learning models from data. We discuss two criteria for Bayesian model selection that are appropriate for repression/classification: one described by Spiegelhalter etah (1993), and other by Buntine (1993). We contrast these two criteria using the prequentia] framework of Dawid (1984), and give sufficient conditions under which the criteria agree.