Communications of the ACM
Statistical analysis with missing data
Statistical analysis with missing data
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Machine Learning - Special issue on inductive transfer
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
The Sample Complexity of Learning Fixed-Structure Bayesian Networks
Machine Learning - Special issue on learning with probabilistic representations
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
A tutorial on learning with Bayesian networks
Learning in graphical models
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Comparing Bayesian network classifiers
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
Semi-supervised learning for facial expression recognition
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning class-discriminative dynamic Bayesian networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Discriminative versus generative parameter and structure learning of Bayesian network classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Augmenting naive Bayes for ranking
ICML '05 Proceedings of the 22nd international conference on Machine learning
Selection of Generative Models in Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A recursive method for discriminative mixture learning
Proceedings of the 24th international conference on Machine learning
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 parameter learning for Bayesian networks
Proceedings of the 25th international conference on Machine learning
Boosted Bayesian network classifiers
Machine Learning
A Discriminative Learning Method of TAN Classifier
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
Latent classification models for binary data
Pattern Recognition
Discriminative model selection for belief net structures
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Generative prior knowledge for discriminative classification
Journal of Artificial Intelligence Research
When discriminative learning of Bayesian network parameters is easy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning coordination classifiers
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning to combine discriminative classifiers: confidence based
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
The Journal of Machine Learning Research
Sequence classification via large margin hidden Markov models
Data Mining and Knowledge Discovery
Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood
The Journal of Machine Learning Research
Sampling of virtual examples to improve classification accuracy for nominal attribute data
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Learning naive bayes for probability estimation by feature selection
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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Bayesian belief nets (BNs) are often used for classification tasks -- typically to return the most likely "class label" for each specified instance. Many BN-learners, however, attempt to find the BN that maximizes a different objective function (viz., likelihood, rather than classification accuracy), typically by first learning an appropriate graphical structure, then finding the maximal likelihood parameters for that structure. As these parameters may not maximize the classification accuracy, "discriminative learners" follow the alternative approach of seeking the parameters that maximize conditional likelihood (CL), over the distribution of instances the BN will have to classify. This paper first formally specifies this task, and shows how it relates to logistic regression, which corresponds to finding the optimal CL parameters for a naïvebayes structure. After analyzing its inherent (sample and computational) complexity, we then present a general algorithm for this task, ELR, which applies to arbitrary BN structures and which works effectively even when given the incomplete training data. This paper presents empirical evidence that ELR works better than the standard "generative" approach in a variety of situations, especially in common situation where the BN-structure is incorrect.