Training method of support vector regression machine

Abstract

The invention provides a training method of a support vector regression machine. The training method concretely includes the steps as follows: firstly, setting a training sample set; secondly, calculating and initializing a kernel function matrix; thirdly, calculating a first training point; fourthly, calculating a second training point; fifthly, calculating a lagrangian multiplier; sixthly, updating an intermediate variable; seventhly, judging whether all samples in the training sample set meet optimal conditions; and eighthly, calculating a regression decision function. The value of the training last time is utilized in the updating of the intermediate variable, so that calculation amount is reduced; and the intermediate variable is utilized fully when the deviation or the falling value of a target function is obtained, so that massive calculation is reduced, the rapid selection of the training points is realized, and the convergence speed of the training is improved.

Description

technical field [0001] The invention belongs to the fields of artificial intelligence, machine learning and data mining, and in particular relates to a training method for a support vector regression machine, which can be widely used in the fields of nonlinear regression, time series analysis and the like. Background technique [0002] The support vector machine (Support Vector Machines, SVM) theory originated from the support vector method proposed by Vapnik for solving pattern recognition problems, and then Vapnik established the ε-support vector regression machine on the basis of the ε-loss function. SVM is constructed based on the principle of structural risk minimization. It has strong learning ability and generalization performance, and can better solve problems such as small samples, high dimensionality, nonlinearity, and local minima. It is widely used in pattern classification and non-linear linear regression. [0003] SVM ultimately comes down to solving a quadrat...

AI Technical Summary

Problems solved by technology

The main disadvantages of this method of selecting training points are: there is no definite principle to judge the severity of violation of the KKT condition, and only suitable training points can be searched through calculation; in addition, this method only considers the progress of the solution when selecting the second training point , but the ultimate goal of solving is to minimize the objective function, so the meaning of this selection method is not clear, and it is likely to cause the objective function to increase during the training process

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