Abstract
In general, the similar input data have the similar output target values. A novel Fast Support Vector Regression (FSVR) is proposed on the reduced training set. Firstly, the improved learning machine divides the training data into blocks by using the traditional clustering methods, such as K-mean and FCM clustering techniques. Secondly, the membership function on each block is defined by the corresponding target values of the training data, all the training data have the membership degree falling into the interval [0, 1], which can vary the penalty coefficient by multiplying C. Thirdly, the reduced training set is used to training FSVR, which consists of the data with the membership degrees, which are greater than or equal to the selected suitable parameter λ. The experimental results on the traditional machine learning data sets show that the FSVR can not only achieve the better or acceptable performance but also downsize the number of training data and speed up training.
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Zhou, W., Xiong, Y., Wu, Ca., Liu, H. (2011). Fast Support Vector Regression Based on Cut. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_44
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DOI: https://doi.org/10.1007/978-3-642-21524-7_44
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