Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamentals of speech recognition
Fundamentals of speech recognition
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
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
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Being Bayesian about Network Structure
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Guest Editors' Introduction to the Special Section on Graphical Models in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques
IEEE Transactions on Knowledge and Data Engineering
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first 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
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning a restricted Bayesian network for object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning Visual Object Categories for Robot Affordance Prediction
International Journal of Robotics Research
Neural Networks
Feature selection for Bayesian network classifiers using the MDL-FS score
International Journal of Approximate Reasoning
Learning graphical models for hypothesis testing and classification
IEEE Transactions on Signal Processing
Learning Bayesian network classifiers by risk minimization
International Journal of Approximate Reasoning
Biclustering-driven ensemble of Bayesian belief network classifiers for underdetermined problems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Learning attentive fusion of multiple bayesian network classifiers
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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The use of Bayesian networks for classification problems has received a significant amount of recent attention. Although computationally efficient, the standard maximum likelihood learning method tends to be suboptimal due to the mismatch between its optimization criteria (data likelihood) and the actual goal of classification (label prediction accuracy). Recent approaches to optimizing classification performance during parameter or structure learning show promise, but lack the favorable computational properties of maximum likelihood learning. In this paper we present boosted Bayesian network classifiers, a framework to combine discriminative data-weighting with generative training of intermediate models. We show that boosted Bayesian network classifiers encompass the basic generative models in isolation, but improve their classification performance when the model structure is suboptimal. We also demonstrate that structure learning is beneficial in the construction of boosted Bayesian network classifiers. On a large suite of benchmark data-sets, this approach outperforms generative graphical models such as naive Bayes and TAN in classification accuracy. Boosted Bayesian network classifiers have comparable or better performance in comparison to other discriminatively trained graphical models including ELR and BNC. Furthermore, boosted Bayesian networks require significantly less training time than the ELR and BNC algorithms.