Instance-Based Learning Algorithms
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
Learning hybrid Bayesian networks from data
Learning in graphical models
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Improving Naive Bayes Using Class-Conditional ICA
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Inference for the Generalization Error
Machine Learning
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
A Bayesian network classifier that combines a finite mixture model and a naïve bayes model
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Latent classification models for binary data
Pattern Recognition
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
A latent model for collaborative filtering
International Journal of Approximate Reasoning
LTC: A latent tree approach to classification
International Journal of Approximate Reasoning
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One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. These models rely on two assumptions: (i) All the attributes used to describe an instance are conditionally independent given the class of that instance, and (ii) all attributes follow a specific parametric family of distributions. In this paper we propose a new set of models for classification in continuous domains, termed latent classification models. The latent classification model can roughly be seen as combining the Naïve Bayes model with a mixture of factor analyzers, thereby relaxing the assumptions of the Naïve Bayes classifier. In the proposed model the continuous attributes are described by a mixture of multivariate Gaussians, where the conditional dependencies among the attributes are encoded using latent variables. We present algorithms for learning both the parameters and the structure of a latent classification model, and we demonstrate empirically that the accuracy of the proposed model is significantly higher than the accuracy of other probabilistic classifiers.