The administrative authority of Taiwan,China has been executing the educational reform programs for more than two decades.However,the so-called between-class ability grouping which is prohibited by The administrative ...The administrative authority of Taiwan,China has been executing the educational reform programs for more than two decades.However,the so-called between-class ability grouping which is prohibited by The administrative authority of Taiwan,China is still found in many places;and Taiwan region of China's cram schools are even more popular and diversified than before.The authors argue that,in addition to individual's socio-economic background,regional characteristics and school attributes also play important roles.Bringing these two factors back in,the causal relationships among ability grouping,cram schooling,and student academic achievement can be analyzed more accurately.Using data from Taiwan region of China Education Panel Survey,the authors'empirical results show that,first of all,in more urbanized area,between-class ability grouping is less popular but cram school participation is wider spread these years.Secondly,the effects of family backgrounds on students'cram school participation are not as critical as they were before.Thirdly,between-class ability grouping and students'performance are positively associated but the internal mechanism still needs further investigation.展开更多
Robust minimum class variance twin support vector machine(RMCV-TWSVM) presented previously gets better classification performance than the classical TWSVM. The RMCV-TWSVM introduces the class variance matrix of positi...Robust minimum class variance twin support vector machine(RMCV-TWSVM) presented previously gets better classification performance than the classical TWSVM. The RMCV-TWSVM introduces the class variance matrix of positive and negative samples into the construction of two hyperplanes. However, it does not consider the total structure information of all the samples, which can substantially reduce its classification accuracy. In this paper, a new algorithm named structural regularized TWSVM based on within-class scatter and between-class scatter(WSBS-STWSVM) is put forward. The WSBS-STWSVM can make full use of the total within-class distribution information and between-class structure information of all the samples. The experimental results illustrate high classification accuracy and strong generalization ability of the proposed algorithm.展开更多
文摘The administrative authority of Taiwan,China has been executing the educational reform programs for more than two decades.However,the so-called between-class ability grouping which is prohibited by The administrative authority of Taiwan,China is still found in many places;and Taiwan region of China's cram schools are even more popular and diversified than before.The authors argue that,in addition to individual's socio-economic background,regional characteristics and school attributes also play important roles.Bringing these two factors back in,the causal relationships among ability grouping,cram schooling,and student academic achievement can be analyzed more accurately.Using data from Taiwan region of China Education Panel Survey,the authors'empirical results show that,first of all,in more urbanized area,between-class ability grouping is less popular but cram school participation is wider spread these years.Secondly,the effects of family backgrounds on students'cram school participation are not as critical as they were before.Thirdly,between-class ability grouping and students'performance are positively associated but the internal mechanism still needs further investigation.
基金supported in part by the National Natural Science Foundation of China (51875457)Natural Science Foundation of Shaanxi Province of China (2021JQ-701)Xi’an Science and Technology Plan Project (2020KJRC0109)。
文摘Robust minimum class variance twin support vector machine(RMCV-TWSVM) presented previously gets better classification performance than the classical TWSVM. The RMCV-TWSVM introduces the class variance matrix of positive and negative samples into the construction of two hyperplanes. However, it does not consider the total structure information of all the samples, which can substantially reduce its classification accuracy. In this paper, a new algorithm named structural regularized TWSVM based on within-class scatter and between-class scatter(WSBS-STWSVM) is put forward. The WSBS-STWSVM can make full use of the total within-class distribution information and between-class structure information of all the samples. The experimental results illustrate high classification accuracy and strong generalization ability of the proposed algorithm.
