Statistical analysis with missing data
Statistical analysis with missing data
Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning - Special issue on learning with probabilistic representations
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
On predictive distributions and Bayesian networks
Statistics and Computing
Classifier Learning with Supervised Marginal Likelihood
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Discriminative Parameter Learning of General Bayesian Network Classifiers
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
When discriminative learning of Bayesian network parameters is easy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On supervised selection of Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Models and selection criteria for regression and classification
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
Discriminatively Trained Markov Model for Sequence Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Discriminative learning of Bayesian network classifiers
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Discriminative vs. Generative Learning of Bayesian Network Classifiers
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Broad phonetic classification using discriminative Bayesian networks
Speech Communication
Bayesian classifiers based on kernel density estimation: Flexible classifiers
International Journal of Approximate Reasoning
Bayesian Inference Under Probability Constraints
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
On Discriminative Parameter Learning of Bayesian Network Classifiers
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Generative prior knowledge for discriminative classification
Journal of Artificial Intelligence Research
Data & Knowledge Engineering
A conditional independence algorithm for learning undirected graphical models
Journal of Computer and System Sciences
Bayesian Network Structure Learning by Recursive Autonomy Identification
The Journal of Machine Learning Research
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
The Journal of Machine Learning Research
IEEE Transactions on Information Forensics and Security
Large margin learning of Bayesian classifiers based on Gaussian mixture models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood
The Journal of Machine Learning Research
Bayesian learning of markov network structure
ECML'06 Proceedings of the 17th European conference on Machine Learning
Learning Bayesian network classifiers by risk minimization
International Journal of Approximate Reasoning
Robust bayesian linear classifier ensembles
ECML'05 Proceedings of the 16th European conference on Machine Learning
Discriminative learning of bayesian network classifiers via the TM algorithm
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Bayesian network classifiers with reduced precision parameters
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
Alleviating naive Bayes attribute independence assumption by attribute weighting
The Journal of Machine Learning Research
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Discriminative learning of the parameters in the naive Bayes model is known to be equivalent to a logistic regression problem. Here we show that the same fact holds for much more general Bayesian network models, as long as the corresponding network structure satisfies a certain graph-theoretic property. The property holds for naive Bayes but also for more complex structures such as tree-augmented naive Bayes (TAN) as well as for mixed diagnostic-discriminative structures. Our results imply that for networks satisfying our property, the conditional likelihood cannot have local maxima so that the global maximum can be found by simple local optimization methods. We also show that if this property does not hold, then in general the conditional likelihood can have local, non-global maxima. We illustrate our theoretical results by empirical experiments with local optimization in a conditional naive Bayes model. Furthermore, we provide a heuristic strategy for pruning the number of parameters and relevant features in such models. For many data sets, we obtain good results with heavily pruned submodels containing many fewer parameters than the original naive Bayes model.