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
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Neural networks for pattern recognition
Neural networks for pattern recognition
Neural network design
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 Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Exact model averaging with naive Bayesian classifiers
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Differential Approach to Inference in Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Inference for the Generalization Error
Machine Learning
Discriminative Parameter Learning of General Bayesian Network Classifiers
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Convex Optimization
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
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
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
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Discriminative Structure Learning of Markov Logic Networks
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
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
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
Discriminative model selection for belief net structures
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
The Journal of Machine Learning Research
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
Smooth receiver operating characteristics (smROC) curves
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
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
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
Learning attentive fusion of multiple bayesian network classifiers
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Bandit-based structure learning for bayesian network classifiers
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Cluster searching strategies for collaborative recommendation systems
Information Processing and Management: an International Journal
A hybrid fuzzy-based personalized recommender system for telecom products/services
Information Sciences: an International Journal
Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm
International Journal of Business Information Systems
International Journal of Business Information Systems
Alleviating naive Bayes attribute independence assumption by attribute weighting
The Journal of Machine Learning Research
International Journal of Approximate Reasoning
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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 parameters for that structure that maximize the likelihood of the data. As these parameters may not maximize the classification accuracy, "discriminative parameter 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, shows how it extends standard logistic regression, and analyzes its inherent sample and computational complexity. We then present a general algorithm for this task, ELR, that applies to arbitrary BN structures and that works effectively even when given incomplete training data. Unfortunately, ELR is not guaranteed to find the parameters that optimize conditional likelihood; moreover, even the optimal-CL parameters need not have minimal classification error. This paper therefore presents empirical evidence that ELR produces effective classifiers, often superior to the ones produced by the standard "generative" algorithms, especially in common situations where the given BN-structure is incorrect.