Sentence examples for dimension classification from inspiring English sources

Three dimension classification and refinement was performed with Relion1.2 (Scheres, 2012).

For example, in high dimension classification, [48], and [22] showed that conventional classification rules using all features perform no better than random guess due to noise accumulation.

While the focus of the voice classification model is centered at the preprocessing steps which leverage the features from both time and frequency domains followed by feature selection for reducing the feature space dimension, classification algorithms can become flexible plug-and-play in our model design.

Three of these categorize motor skills according to one common characteristic of the skill and lead to one-dimension classification systems.

One one-dimension classification system differentiates skills depending on the sizes of the primary muscle groups required to produce an action (gross motor skills vs. fine motor skills).

The third one-dimension classification system makes a distinction according to the stability of the environmental context in which an action is being performed (open motor skills vs. closed motor skills) [ 35, 39].

The self-classifier (the section of the EQ-5D used in this analysis) provides a simple method for capturing self-reported descriptions of health problems according to a 5-dimension classification system.

On this dimension of classification we can recognize registers such as politics and personal relations, and technical registers like biology and mathematics.'.

Although the design and evaluation of face recognition algorithms draw upon some familiar statistical ideas in multivariate statistics, dimension reduction, classification, clustering, binary response data, generalized linear models and random effects, the field also presents some unique features and challenges.

We use a simple Gaussian maximum-likelihood algorithm for all the experiments; i.e., each class is modeled as a multi-dimensional Gaussian, with the number of dimensions matching the feature vector dimension, and classification is then performed by finding the class of maximum likelihood for the test sample [23].

With the further increase of the feature subspace dimension, the classification performance drops rapidly, which indicates that selecting a feature subspace with 10 to 30 dimensions can maximize the balanced relationship between accuracy and diversity of base classifiers.