Sentence examples for features a random from inspiring English sources

Based on these combined features, a random forest (RF) classifier was adopted to classify B-cell epitopes and non-epitopes.

Boondoggle, a Belgian interactive agency, has cooked up a deliciously useless product called TweetNotebook that basically lets you create a physical notebook that features a random set of your Twitter messages.

The 3' tagging was accomplished using the Terminal-Tagging Oligo, which features a random nucleotide sequence at its 3' end, a tagging sequence at its 5' end and a 3'-blocking group on the 3'-terminal nucleotide.

At issue was whether the selection force is the cause of one of the scale-free network features: a random mutation does not harm a network, as a whole, but can cause it to collapse, but only with a deliberate attack on the hubs (nodes that contain many immediate neighbors) [ 13].

For each position of the bit vector or feature, a random and uniform number R i is generated in the range between 0 and 1.

In Experiment 1B, featuring a random sentence presentation, participants were no longer required to memorize and repeat three sentences in a row, rather they repeated each sentence immediately and received immediate auditory feedback.

The vast majority of sequences featured a random distribution of CTL epitopes (> 99%), and a large amount of variation in H&S clustering score per protein, just as seen in HIV-1 (Fig. 6A).

Therefore, instead of training one classifier to cover all the feature space, we separate the features with a random sampling scheme without replacement and keep almost equal dimensions for every subspace.

A characteristic feature of a random walk is the quadratic dependence between distance moved and the number of steps taken [ 33]; e.g. doubling the intersite distance increases the communication time 4-fold.

The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier.

This can partly be explained by how each tree is built in the random forest: the algorithm selects the most informative feature from a random subset of features as the node to split on when building each tree.