Showing papers by "David A. Landgrebe published in 1984"
TL;DR: It is shown that by having nonstationary compatibility coefficients (NSCC's) the PRL stabilizes about the minimum error which is obtained during the early iterations.
Abstract: Current implementation of probabilistic relaxation labeling (PRL) is based on stationary compatibility coefficients (SCC's). Such labeling frequently diverges from an achieved minimum labeling error. In this correspondence it is shown that by having nonstationary compatibility coefficients (NSCC's) the PRL stabilizes about the minimum error which is obtained during the early iterations. Also, a noniterative labeling algorithm which uses NSCC and has a performance similar to that of the modified PRL is suggested.
15 citations
TL;DR: An algorithm is presented that predicts the best feature dimensionality, taking into account the number of training samples, and it is demonstrated that rather small training set sizes are still practical using these techniques.
7 citations