Abstract
The concept of grey system is evolving from the concept of “black box”, mainly focusing on objects with clear extension but vague intension. The core part of grey system theory is dynamic modeling, which has organically combined theory and actual situation, so as to solve and instruct actual problem.Support Vector Machine method is determining decision function according to limited sample information and little support vector quality. The counting process is not relevant to space dimensionality, mainly dealing with novel small sample study method of non-linear regression problems. In this paper, through introducing combining grey system theory and support vector machine theory, combining grey prediction model and support vector machine model, the possibility of combining these two has been attained.
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Shaohua, N. (2013). Small Sample Prediction Based on Grey Support Vector Machine. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 392. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53703-5_58
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DOI: https://doi.org/10.1007/978-3-642-53703-5_58
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