| Title |
Spatial contextual classification based on conditional distributions belonging to elliptical and exponential families / |
| Translation of Title |
Erdvinis kontekstinis klasifikavimas naudojant sąlyginius eliptinių ir eksponentinių šeimų skirstinius. |
| Authors |
Zikarienė, Eglė |
| DOI |
10.15388/vu.thesis.376 |
| Full Text |
|
| Pages |
152 |
| Keywords [eng] |
supervised generative classification ; Bayes discriminant function (BDF) ; elliptical and exponential families conditional distributions |
| Abstract [eng] |
When using supervised generative models in classification problems, the assumption of independence of observations is applied or the data is assumed to be modelled by a Gaussian Random Field (GRF). To solve classification problems for features with spatial contextual information with non-GRF, new classification methods are required. The research aims to construct supervised generative classification algorithms based on Bayes Discriminant Function (BDF) for features with spatial contextual information distributions belonging to exponential and elliptic families. Spatial contextual information is incorporated into the probability distribution of feature values and class labels. The properties of auto-models are used to construct exponential family models. BDF is defined by the ratio of logarithms of univariate conditional probability density (mass) functions. Expressions of error rates are obtained for the accuracy assessment. The proposed classifiers are investigated using simulated and real data, and compared using different class label functions. This work extends the application of supervised generative classification methods for features with distributions of spatial contextual information described by GRF and non-GRF models. An algorithm based on BDF has been developed, which allows using supervised generative classification models for feature values with distributions belonging to the exponential and elliptical families. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Doctoral thesis |
| Language |
English |
| Publication date |
2022 |
