… After generation we perform a computationally cheap validity test by inferring bonds and …
substantial diversity in configuration space, the diversity in chemical space is limited. This can …

… In this paper, the Euclidean Distance, which is the most prevalent distance metric in the …
or moderately high dimensional space. However, they could lose their effectiveness and …

… to Euclidean distances of points placed in some (possibly high-dimensional) space. …
quadratic form qh on the higherdimensional space Rh. The resulting norm ∥x∥q := √qh(x) …

… both high query efficiency and high recall empirically on majority of the datasets under a
wide range of settings. … The euclidean distance is one of the most widely used measure on …

… effective method for satisfactory analysis of high dimensional … manifolds, the Euclidian
distance between the vertices … , the shortest distance known as geodesic distance is introduced …

… So, this paper presents a unique feature ordering and selection technique called Feature
Ranking and Subset Selection based on Euclidean distance (FRSSED). Two bearing …

… Moreover, it entails the efficacy to envisage ingeniously the vast and heterogeneous … It is
obvious that under such a situation, Euclidean distance will render a poor measure of similarity. …

… high dimensionality that fail to retain the effectiveness of … for high dimensionality data sets
unless the runtime is improved. Nevertheless, Euclidean distance is the most common distance …

… we previously introduced for virtual screening tasks is used to increase efficiency [45]. The
\(c\)… dimensional space after embedding based on topological and Euclidean distance for data …

Praise for the first edition: "[This book] succeeds singularly at providing a structured
introduction to this active field of research. … it is arguably the most accessible overview yet …