scholarly journals Error rates in spatial classification of Gaussian data with random labeling

2010 ◽  
Vol 51 ◽  
Author(s):  
Lijana Stabingienė ◽  
Kęstutis Dučinskas
Keyword(s):  
Random Field ◽  
Error Rate ◽  
Field Model ◽  
Gaussian Random Field ◽  
Parametric Uncertainty ◽  
Error Rates ◽  
Spatial Classification ◽  
Random Labeling ◽  
Gaussian Data

In spatial classification it is usually assumed that features observations given labels are independently distributed. We have retracted this assumption by proposing stationary Gaussian random field model for features observations. The label are assumed to follow Disrete Random Field (DRF) model. Formula for exact error rate based on Bayes discriminant function (BDF) is derived. In the case of partial parametric uncertainty (mean parameters and variance are unknown), the approximation of the expected error rate associated with plug-in BDF is also derived. The dependence of considered error rates on the values of range and clustering parameters is investigated numerically for training locations being second-order neighbors to location of observation to be classified.

scholarly journals Supervised linear classification of Gaussian spatio-temporal data

2021 ◽  
Vol 62 ◽  
pp. 9-15
Author(s):  
Marta Karaliutė ◽  
Kęstutis Dučinskas
Keyword(s):  
Time Moment ◽  
Gaussian Random Field ◽  
Covariance Structure ◽  
Simulated Data ◽  
Spatial Location ◽  
Error Rates ◽  
Prior Probabilities ◽  
Structure Factors ◽  
Spatio Temporal

In this article we focus on the problem of supervised classifying of the spatio-temporal Gaussian random field observation into one of two classes, specified by different mean parameters. The main distinctive feature of the proposed approach is allowing the class label to depend on spatial location as well as on time moment. It is assumed that the spatio-temporal covariance structure factors into a purely spatial component and a purely temporal component following AR(p) model. In numerical illustrations with simulated data, the influence of the values of spatial and temporal covariance parameters to the derived error rates for several prior probabilities models are studied.

scholarly journals Effect of anisotropy coeficient on error rates of linear discriminant functions

2021 ◽  
Vol 47 ◽  
Author(s):  
Kęstutis Dučinskas ◽  
Lina Dreižienė
Keyword(s):  
Spatial Data ◽  
Supervised Classification ◽  
Statistical Approach ◽  
Gaussian Random Field ◽  
Error Rates ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Recognition Error ◽  
Two Populations

Paper deals with statistical classification of spatial data as a part of widely applicable statistical approach to pattern recognition. Error rates in supervised classification of Gaussian random field observation into one of two populations specified by different constant means and common stationary geometric anisotropic covariance are considered. Formula for the exact Bayesian error rate is derived. The influence of the ratio of anisotropy to the error rates is evaluated numerically for the case of complete parametric certainty.

Interface Style and Error Rates - Some Experimental Results

2000 ◽  
Vol 44 (22) ◽  
pp. 595-598
Author(s):  
Peter F. Elzer ◽  
Badi Boussoffara ◽  
Carsten Beuthel
Keyword(s):  
Comparative Study ◽  
Error Rate ◽  
Error Rates ◽  
Experimental Results ◽  
Research Project

At the IPP a number of new forms of visualizations of process values have been developed. Several of them have been evaluated by user experiments. In the context of a research project (supported by the Volkswagen Foundation in Germany (Ref. Nr.: I/69 886)) a comparative study with respect to the influence of the interface style on the error rate during classification of process states was undertaken. The paper describes the results and discusses them in a taxonomy context.

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