Bayesian spatial binary classification

Bayesian Classification

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Hybrid learning of Bayesian multinets for binary classification

We propose a scoring criterion, named mixture-based factorized conditional log-likelihood (mfCLL), which allows for efficient hybrid learning of mixtures of Bayesian networks in binary classification tasks. The learning procedure is decoupled in foreground and background learning, being the foreground the single concept of interest that we want to distinguish from a highly complex background. T...

Bayesian Prediction in Spatial Data Analysis

A Linear Bayesian Updating Model for Probabilistic Spatial Classification

Abstract: Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. To solve the challenge, this study introduces the theoretical backgrounds of the linear Bayesian updating (LBU) model for spatial classifica...

Binary classification

The problem of binary classification can be stated as follows. We have a random couple Z = (X ,Y ), where X ∈ Rd is called the feature vector and Y ∈ {−1,1} is called the label1. In the spirit of the modelfree framework, we assume that the relationship between the features and the labels is stochastic and described by an unknown probability distribution P ∈P (Z), where Z=Rd × {−1,1}. As usual, ...