The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. It can be interpreted as an empirical Bayes classifier based on the estimated a posteriori probabilities from the k nearest neighbors. The performance of the k-nearest neighbor rule relies on the locally constant a posteriori probability assumption. This assumption, however, becomes problem...

The aim of our research is to classify digital mammograms into two classes, abnormal microcalcification and normal. Texture is one of the major mammographic characteristics. The statistical textural of Gray Level Coocurrence Matrix (GLCM) used in characterizing images are contrast, energy and entropy. K-Nearest Neighbor (K-NN) and Fuzzy K-Nearest Neighbor (FK-NN) was proposed for classifying im...

Although double sampling has been shown to be an effective method to estimate timber volume in forest inventories, only a limited body of research has tested the effectiveness of double sampling on forest biomass estimation. From forest biomass inventories collected over 9,683 ha using systematic point sampling, we examined how a double sampling scheme would have affected precision and efficien...

In a recent paper [13], the Fast Marching farthest point sampling strategy (FastFPS) for planar domains and curved manifolds was introduced. The version of FastFPS for curved manifolds discussed in the paper [13] deals with surface domains in triangulated form only. Due to a restriction of the underlying Fast Marching method, the algorithm further requires the splitting of any obtuse into acute...