Estimation of the probability of error without ground truth and known a priori probabilitiesThe probability of error or, alternatively, the probability of correct classification (PCC) is an important criterion in analyzing the performance of a classifier. Labeled samples (those with ground truth) are usually employed to evaluate the performance of a classifier. Occasionally, the numbers of labeled samples are inadequate, or no labeled samples are available to evaluate a classifier's performance; for example, when crop signatures from one area from which ground truth is available are used to classify another area from which no ground truth is available. This paper reports the results of an experiment to estimate the probability of error using unlabeled test samples (i.e., without the aid of ground truth).
Document ID
19770032220
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Havens, K. A. (Lockheed Electronics Co. Houston, TX, United States)
Minster, T. C. (Lockheed Electronics Co. Houston, TX, United States)
Thadani, S. G. (Lockheed Electronics Co., Inc. Aerospace Systems Div., Houston, Tex., United States)
Date Acquired
August 9, 2013
Publication Date
January 1, 1976
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: Symposium on Machine Processing of Remotely Sensed Data