Quantum machine learning has shown advantages in many ways compared to classical learning. In learning, a difficult problem is how learn model with high robustness and strong generalization ability from limited feature space. Combining multiple models as base learners, ensemble (EL) can effectively improve the accuracy, of final model. The key EL lies two aspects, performance learners choice co...

One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...

In this paper we propose a hybrid symbol classifier within a global framework for online handwritten mathematical expression recognition. The proposed architecture aims at handling mathematical expression recognition as a simultaneous optimization of symbol segmentation, symbol recognition, and 2D structure recognition under the restriction of a mathematical expression grammar. To improve the c...