Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Neural networks and the bias/variance dilemma
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
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
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Unsupervised stratification of cross-validation for accuracy estimation
Artificial Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Introduction to Algorithms
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Machine Learning
Variance and Bias for General Loss Functions
Machine Learning
Semi-Naive Bayesian Classifier
EWSL '91 Proceedings of the European Working Session on Machine Learning
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Projection Pursuit Fitting Gaussian Mixture Models
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
M-Kernel Merging: Towards Density Estimation over Data Streams
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
Rapid Spline-based Kernel Density Estimation for Bayesian Networks
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Learning Bayesian Networks
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
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
Learning hybrid Bayesian networks using mixtures of truncated exponentials
International Journal of Approximate Reasoning
Bayesian network classification using spline-approximated kernel density estimation
Pattern Recognition Letters
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Information theory and classification error in probabilistic classifiers
DS'06 Proceedings of the 9th international conference on Discovery Science
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
Naive bayes classifiers that perform well with continuous variables
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Projection pursuit mixture density estimation
IEEE Transactions on Signal Processing
Bayesian fluorescence in situ hybridisation signal classification
Artificial Intelligence in Medicine
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Feature selection for Bayesian network classifiers using the MDL-FS score
International Journal of Approximate Reasoning
From Bayesian classifiers to possibilistic classifiers for numerical data
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
Domain selection and adaptation in smart homes
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
Genetic algorithms to simplify prognosis of endocarditis
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
On the properties of concept classes induced by multivalued Bayesian networks
Information Sciences: an International Journal
Algorithms for maximum-likelihood bandwidth selection in kernel density estimators
Pattern Recognition Letters
A Bayesian network for burr detection in the drilling process
Journal of Intelligent Manufacturing
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
International Journal of Approximate Reasoning
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When learning Bayesian network based classifiers continuous variables are usually handled by discretization, or assumed that they follow a Gaussian distribution. This work introduces the kernel based Bayesian network paradigm for supervised classification. This paradigm is a Bayesian network which estimates the true density of the continuous variables using kernels. Besides, tree-augmented naive Bayes, k-dependence Bayesian classifier and complete graph classifier are adapted to the novel kernel based Bayesian network paradigm. Moreover, the strong consistency properties of the presented classifiers are proved and an estimator of the mutual information based on kernels is presented. The classifiers presented in this work can be seen as the natural extension of the flexible naive Bayes classifier proposed by John and Langley [G.H. John, P. Langley, Estimating continuous distributions in Bayesian classifiers, in: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 1995, pp. 338-345], breaking with its strong independence assumption. Flexible tree-augmented naive Bayes seems to have superior behavior for supervised classification among the flexible classifiers. Besides, flexible classifiers presented have obtained competitive errors compared with the state-of-the-art classifiers.