Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Elements of information theory
Elements of information theory
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
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
The Sample Complexity of Learning Fixed-Structure Bayesian Networks
Machine Learning - Special issue on learning with probabilistic representations
Learning in graphical models
Learning Markov networks: maximum bounded tree-width graphs
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Maximum likelihood bounded tree-width Markov networks
Artificial Intelligence
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Natural statistical models for automatic speech recognition
Natural statistical models for automatic speech recognition
Discriminative, generative and imitative learning
Discriminative, generative and imitative learning
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first 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
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The Journal of Machine Learning Research
Modified MMI/MPE: a direct evaluation of the margin in speech recognition
Proceedings of the 25th international conference on Machine learning
Nonparametric Independence Tests: Space Partitioning and Kernel Approaches
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Broad phonetic classification using discriminative Bayesian networks
Speech Communication
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
Bayesian network classifiers versus selective k-NN classifier
Pattern Recognition
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning Bayesian network classifiers by risk minimization
International Journal of Approximate Reasoning
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
Alleviating naive Bayes attribute independence assumption by attribute weighting
The Journal of Machine Learning Research
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We introduce a simple order-based greedy heuristic for learning discriminative structure within generative Bayesian network classifiers. We propose two methods for establishing an order of N features. They are based on the conditional mutual information and classification rate (i.e., risk), respectively. Given an ordering, we can find a discriminative structure with O(Nk+1) score evaluations (where constant k is the tree-width of the sub-graph over the attributes). We present results on 25 data sets from the UCI repository, for phonetic classification using the TIMIT database, for a visual surface inspection task, and for two handwritten digit recognition tasks. We provide classification performance for both discriminative and generative parameter learning on both discriminatively and generatively structured networks. The discriminative structure found by our new procedures significantly outperforms generatively produced structures, and achieves a classification accuracy on par with the best discriminative (greedy) Bayesian network learning approach, but does so with a factor of ~10-40 speedup. We also show that the advantages of generative discriminatively structured Bayesian network classifiers still hold in the case of missing features, a case where generative classifiers have an advantage over discriminative classifiers.