On-line mixture-based alternative to logistic regression

Title:

On-line mixture-based alternative to logistic regression

Creator:

Nagy, I. and Suzdaleva , E.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:038c79b5-f0aa-468b-9d79-4c33fc668591
uuid:038c79b5-f0aa-468b-9d79-4c33fc668591
doi:10.14311/NNW.2016.26.024

Subject:

on-line modeling, on-line logistic regression, recursive mixture estimation, and data dependent pointer

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:5

Harvested from:

CDK

Metadata only:

false