Classification of Imaginary motor task from Electroencephalographic Signals: A Comparison of Feature Selection Methods and Classification Algorithms

In this work, we extracted facial local features using local binary pattern (LBP) and then, we fuse these features with clothing features, which enhance the classification

In chapter 4 we will justify that Recursive Maxima Hunting finds the points that appear in the Bayes rule when the mean of the second class is piecewise linear and the noise

In any case, the work of (Bashashati et al. [3]) deeply explains the different methods and signal processing algorithms used in the different steps, such as pre-processing,

- Distributed multi-label selection methods for continuous features: Proposing the implementation of two distributed feature selection methods on continuous features for

The first method is based on color and texture features extraction, followed by a selection of the most effective features, and classification with a hybrid of artificial

These techniques are also known as sequential agglomerative hierarchical non-overlapping (SAHN) methods. The performance of these algorithms departs from generating a

For the second case of this study, the features for training the SVM will be extracted from the brain sources estimated using the selected SLMs MLE, MNE and wMNE. The scenario is

the RoboEarth ontology; (2) knowledge-based methods for using prior information, exemplified in the selection of object models for exploration and in the guidance of a robot