In this paper, we prove that there exists a infinite set of non-trivial local Fitting classes every element in which is decomposable as a non-trivial product of Fitting classes such that every factor in the product is...In this paper, we prove that there exists a infinite set of non-trivial local Fitting classes every element in which is decomposable as a non-trivial product of Fitting classes such that every factor in the product is neither local nor a formation. In particular, this gives a positive answer to Problem 11.25 a） in The Kourovka Notebook.展开更多
In this paper, we study the cover-avoid property of -injectors on chief factors of a group G for a Fitting class in some universe with partial solubility.
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
文摘In this paper, we prove that there exists a infinite set of non-trivial local Fitting classes every element in which is decomposable as a non-trivial product of Fitting classes such that every factor in the product is neither local nor a formation. In particular, this gives a positive answer to Problem 11.25 a） in The Kourovka Notebook.
基金Research of the first author is supported by the Natural Science Foundation of Sandong Province （No. ZR2014AL001）, China.
文摘In this paper, we study the cover-avoid property of -injectors on chief factors of a group G for a Fitting class in some universe with partial solubility.
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.
