Sentence examples for approaches for supervised from inspiring English sources

Linear Discriminant Analysis (LDA) is one of the most popular approaches for supervised feature extraction and dimension reduction.

Among the existing neural approaches for supervised learning of a function, we have selected the grow and learn network for its properties adapted to robotic problems: incrementality and flexible structure.

In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection (SDPP) and, we investigate its applicability to monitoring material's properties from spectroscopic observations.

In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection and, we investigate its applicability to monitoring material's properties from spectroscopic observations.

Of course this does not mean that the naïve Bayes technique is the best approach for supervised classification problems.

In this article, we used a novel approach for supervised module discovery from R-dimensional genomic data, a topic that was not covered by the aforementioned methods.

An experimental evaluation is conducted to show the performance of the SDPP and compare it with a number of state-of-the-art approaches for unsupervised and supervised dimensionality reduction.

The reviewers are correct that the term 'clustering' is generally used for unsupervised approaches in pattern recognition, whereas the term classification is used for supervised approaches.

Bearing this in mind, some approaches have been proposed for supervised pitch extraction with a consequent separation scheme [17 19].

Generally, the co-training style algorithm is a classic approach for semi-supervised learning, which trains in alternation to maximize the mutual agreement on different views.

One important observation that can be made based on our results is that the distribution of the newly labeled instances among subclassifiers in order to ensure subclassifier diversity is a useful approach for semi-supervised ensembles.