The nature of statistical learning theory
The nature of statistical learning theory
Geometry and invariance in kernel based methods
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Dual /spl nu/-support vector machine with error rate and training size biasing
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
IEEE Transactions on Neural Networks
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Due to the complexity of the quality control of higher education and its influence factors, it has always been difficult to have a control on the quality of higher education so as to realize the quantification analysis and give a prediction for the future quality. The ordinary ways of regression analysis have difficulty in establishing models and may lead to "over learning". The support vector machine (SVM) does not have a strict requirement on the number of samples, the distribution of process errors and sample points, and is easy to promote. In this paper, We make a SVM regression analysis of the quality control and prediction of higher education and put forward a regression model with strong generalization ability from the angle of machine learning. The results of the effect of fitting are good under the Kolmogorov-Smirnov (KS) test. Thus, the problems of establishing models, making quantification analysis in the quality control of higher education can have a solution.