The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class cla...The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs.Rest to disassemble the multi-class classification task into binary classification tasks.K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs.Finally,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained together.With the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary classifier.The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset.展开更多
Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from freque...Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.展开更多
The task-based approach has been gaining more and more popularity in recent years. Unlike the traditional language teaching approach, which puts much emphasis on the teaching and learning of language forms and skills,...The task-based approach has been gaining more and more popularity in recent years. Unlike the traditional language teaching approach, which puts much emphasis on the teaching and learning of language forms and skills, it regards the language process as one of learning through doing, emphasizes the central role of meaning in language use and insists that students should learn more effectively if they are fully engaged in a language task. However, due to the lack of an appropriate language environment and limited time for English study, Chinese students may not make as much progress as expected in the unconscious internalization of authentic language by focusing primarily on meaning. In fact the meaning of a language can not be separated from its form. The accurate expression of meaning depends on the proper use of language form. Therefore, in the application of the taskbased approach, we should not neglect the study of language form. This paper analyses the causes for Chinese students' low spoken English proficiency, describes the theoretical foundation and actual practice of the task-based approach, discusses its implications for teaching oral English and makes some proposals for its appropriate application.展开更多
Interaction between learners under group work setting is considered to be significantly influenced by task types. The present empirical study was designed to explore interaction characteristics under convergent tasks ...Interaction between learners under group work setting is considered to be significantly influenced by task types. The present empirical study was designed to explore interaction characteristics under convergent tasks and divergent tasks from three aspects: language production, meaning negotiation and attention to form while performing different types of tasks. The results reveal that there was significant statistical difference in the total language production between two types of tasks. In terms of the occurrence of meaning negotiation and the extent to which students paid attention to language form, there were no significant difference between the two task types.展开更多
基金supported by the National Natural Science Foundation of China(61703131 61703129+1 种基金 61701148 61703128)
文摘The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs.Rest to disassemble the multi-class classification task into binary classification tasks.K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs.Finally,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained together.With the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary classifier.The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset.
基金This work was supportedin part by the National Natural Science Foundation of China(No.60271025,No.30370395)in part by the Science and Technology Depart ment of Shaanxi Province(No.2003K10-G24).
文摘Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.
文摘The task-based approach has been gaining more and more popularity in recent years. Unlike the traditional language teaching approach, which puts much emphasis on the teaching and learning of language forms and skills, it regards the language process as one of learning through doing, emphasizes the central role of meaning in language use and insists that students should learn more effectively if they are fully engaged in a language task. However, due to the lack of an appropriate language environment and limited time for English study, Chinese students may not make as much progress as expected in the unconscious internalization of authentic language by focusing primarily on meaning. In fact the meaning of a language can not be separated from its form. The accurate expression of meaning depends on the proper use of language form. Therefore, in the application of the taskbased approach, we should not neglect the study of language form. This paper analyses the causes for Chinese students' low spoken English proficiency, describes the theoretical foundation and actual practice of the task-based approach, discusses its implications for teaching oral English and makes some proposals for its appropriate application.
文摘Interaction between learners under group work setting is considered to be significantly influenced by task types. The present empirical study was designed to explore interaction characteristics under convergent tasks and divergent tasks from three aspects: language production, meaning negotiation and attention to form while performing different types of tasks. The results reveal that there was significant statistical difference in the total language production between two types of tasks. In terms of the occurrence of meaning negotiation and the extent to which students paid attention to language form, there were no significant difference between the two task types.
