Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Numerical recipes in C (2nd ed.): the art of scientific computing
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The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
A tutorial on learning with Bayesian networks
Learning in graphical models
Maximum conditional likelihood via bound maximization and the CEM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Robust Classification for Imprecise Environments
Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Eighteenth national conference on Artificial intelligence
Tree Induction for Probability-Based Ranking
Machine Learning
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
Discriminatively Trained Markov Model for Sequence Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Selection of Generative Models in Classification
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Classification using Hierarchical Naïve Bayes models
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Discriminative learning of Bayesian network classifiers
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Discriminative parameter learning for Bayesian networks
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Boosted Bayesian network classifiers
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Discriminative Structure Learning of Markov Logic Networks
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A Discriminative Learning Method of TAN Classifier
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Broad phonetic classification using discriminative Bayesian networks
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Latent classification models for binary data
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Competitive generative models with structure learning for NLP classification tasks
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On the classification performance of TAN and general Bayesian networks
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nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A conditional independence algorithm for learning undirected graphical models
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K-Distributions: A New Algorithm for Clustering Categorical Data
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Regularized margin-based conditional log-likelihood loss for prototype learning
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Fuzzy Sets and Systems
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Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when learned in the standard way. This is attributable to a mismatch between the objective function used (likelihood or a function thereof) and the goal of classification (maximizing accuracy or conditional likelihood). Unfortunately, the computational cost of optimizing structure and parameters for conditional likelihood is prohibitive. In this paper we show that a simple approximation---choosing structures by maximizing conditional likelihood while setting parameters by maximum likelihood---yields good results. On a large suite of benchmark datasets, this approach produces better class probability estimates than naive Bayes, TAN, and generatively-trained Bayesian networks.