@inproceedings{62c8779871d14ebfa9acf3ce11db97ae,

title = "A Comparison of Markov Random Field and Spatial Regression Models for Mining Geospatial Data",

abstract = "A spatial metric, which can be used to systematically calculate the regularization parameter in an MRF formulation of a spatial classification problem was proposed. The standard way to measure classification accuracy is to calculate the percentage of correctly classified objects. Spatial accuracy achieved by classical regression, Spatial Autoregressive Regression (SAR), and Markov Random Field (MRF_hMETIS) was compared using Average Distance to Nearest Prediction (ANDP). It was observed that spatial regression takes two orders of magnitude more computation time relative to MRF_hMETIS approach using the public domain code.",

author = "Sanjay Chawla and Shashi Shekhar and Weili Wu",

year = "2002",

language = "English",

isbn = "0970789017",

series = "Proceedings of the Joint Conference on Information Sciences",

pages = "245--250",

editor = "J.H. Caulfield and S.H. Chen and H.D. Cheng and R. Duro and J.H. Caufield and S.H. Chen and H.D. Cheng and R. Duro and V. Honavar",

booktitle = "Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002",

note = "null ; Conference date: 08-03-2002 Through 13-03-2002",

}