A useful method for representing Bayesian classifiers is through discriminant functions. Here, using copula functions, we propose a new model for discriminants. This model provides a rich and generalized class of decision boundaries. These decision boundaries significantly boost the classification accuracy especially for high dimensional feature spaces. We strengthen our analysis through simula...

Classifying human cognitive states using fMRI data is an interesting and challenging problem that would guide us to better understanding about human brain. In this paper we introduce TAN classifier to distinguish whether subjects are examining a sentence or a picture. Experiments show that using this TAN classifiers provides significantly better results than using Naive Bayes classifiers.

Probabilistic record linkage has been well investigated in recent years. The Fellegi-Sunter probabilistic record linkage and its enhanced version are commonly used methods, which calculate match and non-match weights for each pair of corresponding fields of record-pairs. Bayesian network classifiers – naive Bayes classifier and TAN have also been successfully used here. Very recently, an extend...

This paper makes use of Hierarchical Bayes Models to model and estimate spatial health effects. We focus on Germany, combining rich individual-level household panel data with administrative county– level information to estimate spatial county-level health dependencies. As dependent variable, we use the generic, continuous, and quasi-objective SF12 health measure. Our findings reveal strong and ...

Feature selection has proved to be an effective way to reduce the model complexity while giving a relatively desirable accuracy, especially, when data is scarce or the acquisition of some feature is expensive. However, the single selected model may not always generalize well for unseen test data whereas other models may perform better. Bayesian Model Averaging (BMA) is a widely used approach to...

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A max-2-connected Bayes network is one where there are at most 2 distinct directed paths between any two nodes. We show that even for this restricted topology, null-evidence belief updating is hard to approximate.

Bayesian Classifiers are widely used in machine learning supervised models where there is a reasonable reliability in the dependent variable. This work aims to create a risk measurement model of companies that negotiate with the government using indicators grouped into four risk dimensions: operational capacity, history of penalties and findings, bidding profile, and political ties. It is expec...

We propose a lawn weed detection method modified from our previous work, i.e., Bayesian classifier based method. The proposed method employs features calculated from not only the edge-strength of weed/lawn textures but also color information of RGB. Instead of using Bayesian classifier, we exploit more sophisticated classifier, i.e., supportvector machine, for detecting weeds. After weed detect...