A Neural Network Model for Learning Data Stream with Multiple Class Labels

2011 ◽  
Author(s):  
T. Takata ◽  
S. Ozawa
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Stream ◽  
Class Labels ◽  
Multiple Class ◽  
Learning Data

scholarly journals Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

2020 ◽  
Vol 12 (8) ◽  
pp. 137
Author(s):  
Bo Jiang ◽  
Yanbai He ◽  
Rui Chen ◽  
Chuanyan Hao ◽  
Sijiang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Teaching And Learning ◽  
Large Scale ◽  
Small Scale ◽  
Teaching Improvement ◽  
Data Feedback ◽  
Learning Data

Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.

scholarly journals A Novel Neural Network Model based on Cerebral Hemispheric Asymmetry for EEG Emotion Recognition

2018 ◽  
Author(s):  
Yang Li ◽  
Wenming Zheng ◽  
Zhen Cui ◽  
Tong Zhang ◽  
Yuan Zong
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Training Data ◽  
Local Domain ◽  
Data Representations ◽  
Testing Data ◽  
Left And Right ◽  
Class Labels

In this paper, we propose a novel neural network model, called bi-hemispheres domain adversarial neural network (BiDANN), for EEG emotion recognition. BiDANN is motivated by the neuroscience findings, i.e., the emotional brain's asymmetries between left and right hemispheres. The basic idea of BiDANN is to map the EEG feature data of both left and right hemispheres into discriminative feature spaces separately, in which the data representations can be classified easily. For further precisely predicting the class labels of testing data, we narrow the distribution shift between training and testing data by using a global and two local domain discriminators, which work adversarially to the classifier to encourage domain-invariant data representations to emerge. After that, the learned classifier from labeled training data can be applied to unlabeled testing data naturally. We conduct two experiments to verify the performance of our BiDANN model on SEED database. The experimental results show that the proposed model achieves the state-of-the-art performance.

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