Latent variable models and factors analysis
Latent variable models and factors analysis
Instance-Based Learning Algorithms
Machine Learning
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
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An Introduction to Variational Methods for Graphical Models
Machine Learning
Probabilistic visualisation of high-dimensional binary data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Bayesian parameter estimation via variational methods
Statistics and Computing
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth 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
Eighteenth national conference on Artificial intelligence
Variational methods for inference and estimation in graphical models
Variational methods for inference and estimation in graphical models
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Machine Learning
Noisy-or classifier: Research Articles
International Journal of Intelligent Systems - Uncertainty Processing
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
When discriminative learning of Bayesian network parameters is easy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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 variational approximation for Bayesian networks with discrete and continuous latent variables
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
LTC: A latent tree approach to classification
International Journal of Approximate Reasoning
A survey on latent tree models and applications
Journal of Artificial Intelligence Research
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One of the simplest, and yet most consistently well-performing set of classifiers is the naive Bayes models (a special class of Bayesian network models). However, these models rely on the (naive) assumption that all the attributes used to describe an instance are conditionally independent given the class of that instance. To relax this independence assumption, we have in previous work proposed a family of models, called latent classification models (LCMs). LCMs are defined for continuous domains and generalize the naive Bayes model by using latent variables to model class-conditional dependencies between the attributes. In addition to providing good classification accuracy, the LCM has several appealing properties, including a relatively small parameter space making it less susceptible to over-fitting. In this paper we take a first step towards generalizing LCMs to hybrid domains, by proposing an LCM for domains with binary attributes. We present algorithms for learning the proposed model, and we describe a variational approximation-based inference procedure. Finally, we empirically compare the accuracy of the proposed model to the accuracy of other classifiers for a number of different domains, including the problem of recognizing symbols in black and white images.