Distance function is a main metrics of measuring the affinity between two data points in machine learning. Extant distance functions often provide unreachable values real applications. This can lead to incorrect measure points. paper proposes reachable for KNN classification. The not geometric direct-line It gives consideration class attribute training dataset when Concretely speaking, includes...

A stochastic distance function frontier was estimated using data from a national survey of organic farmers to evaluate the effect of farm-specific attributes on efficiency. Farm-specific and regional variables that shift efficiency were incorporated into the multioutput distance function, including organic farming experience, use of soil-improving inputs, and farmer involvement in research. Par...

this study was conducted to determine a relationship between energy input and yield in greenhouse basil production in esfahan province, iran. data were collected from 26 greenhouse basil producers through a face-to-face questionnaire. the data collected belonged to the production period of 2009–2010 with the following results obtained. a total energy input of 236,057 mj ha-1 was estimated to be...

Distance-based phylogenetic reconstruction methods use the evolutionary distances between species in order to reconstruct the tree spanning them. The evolutionary distance between two species, which is computed from their DNA (or protein) sequences, is typically considered as a fixed function of these sequences, predetermined by the assumed model of evolution. This article continues the line of...

this paper deals with the basic notions of k-tautimmersions . these notions come from two special cases; that is, tight and taut immersions. tight and taut based on high and distance functions respectively and their basic notions are normal bundle, endpoint map, focal point, critical normal. we generalize hight and distance functions to cylindrical function and define basic notions of k-taut im...

A node splitting network that classiies binary patterns by Hamming distance has been presented in 5]. There it has been shown that the node splitting training algorithm can produce a network with much fewer connections than Lippmann's Hamming net 9]. Now this network (NOSDIC) has been further developed to process integer values. For a pattern recognition problem in mobile radio communications t...