These data demonstrate that cognitive bias can impact forensic anthropological non-metric methods on skeletal remains and affects the interpretation and conclusions of the forensic scientists.
A comparison on metric methods is available in [ 17], where it was found that simple metrics work sufficiently well for measuring RNA secondary structure conservation.
Then we establish multi-scale clutter metric method based on LoG operator to describe scenes feature for controlled suppression method.
The Euclidean distance metric method, as implemented in DNASTAR v5.1.2.
We compare the proposed method against SVM, writer-dependent and writer-independent Discriminative Deep Metric Learning method on four offline signature datasets (UTSig, MCYT-75, GPDSsynthetic, and GPDS960GraySignatures) using Histogram of Oriented Gradients (HOG) and Discrete Radon Transform (DRT) features.
It is a variable-metric method with inexact line search, a stable metric updating scheme, and checks for positive-definiteness.
Table 2 gives the metric values for all three methods on each instance, the instance name indicating the number of items.
In this paper, we propose a new methodology that introduces a new metric (energy expenditure), we compare existing methods on a large set of target motion patterns and present a comparison between the simulation and field experiments on proposed target motion patterns.
However, the above metric learning methods just focus on a holistic metric, which discard the geometric structure of human objects and thus affect the discriminative power.
This suggests that the choice of performance metric can have an impact on the rankings between methods on a particular task.
M-tree [10] is the first balanced dynamic ball partitioning metric access method based on fixed-size disk pages.
