On-line Mixture-model-based Logistic Regression

The paper deals with a problem of modeling discrete variables depending on continuous  variables. This problem is known as the logistic regression estimated by  numerical methods. The paper approaches the problem via the recursive Bayesian estimation of mixture models with the purpose of exploring a possibility of constructing the continuous data dependent switching model that should be estimated on-line.  Here the model of the discrete variable dependent on continuous data is represented as the model of the mixture pointer dependent on data from mixture components via their parameters, which switch according to the activity of the components. On-line estimation of the data dependent pointer model has a great potential for tasks of clustering and classification. The specific subproblems include (i) the model parameter estimation both of the pointer and of the components obtained  during the learning phase, and (ii) prediction of the pointer value during the testing phase. These two phases can be joined together in the case of necessity. A real-data experimental comparison with theoretical counterparts shows a competitiveness of the approach in the discussed field.