Sentence examples for robust classification from inspiring English sources

Random Forest is a robust classification algorithm that can be employed in the virtual screening.

Combining these approaches, a robust classification framework can be constructed, which offers three major advantages.

That means a combination of the two algorithms can provide a more robust classification algorithm.

Ensemble learning can be employed to obtain more robust classification schemes.

To tackle this problem, we construct a flexible Tri-partition Neighborhood for robust classification.

Such a treatment results in a more robust classification of intrusions, allowing potential discovery of rare (new) attacks.

In the past few years, a series of robust classification algorithms based on low-rank representation have been put forward.

It is the process of combining information from different sensors to provide a more stable and more robust classification decisions.

We observed that the reduction of descriptors even at > =0.6 correlation cutoff, is sufficient to develop a robust classification model.

The purpose of selecting k-NN classifier over other robust classification methods such as SVM and MLP is to make a preliminary evaluation of the ranked features.

Obviously, ChemSAR has the capacity to obtain the reliable and robust classification model for the evaluation of Caco-2 Cell permeability.