SVM (support vector machine) classifier training sample acquiring method, training method and training system

Abstract

The invention provides an SVM (support vector machine) classifier training sample acquiring method, a training method and a training system. The SVM classifier training sample acquiring method includes calculating to acquire distance of each sample of an SVM classifier; according to the distance of each sample, clustering the samples for the first time to acquire at least one first category and the samples contained by each first category; clustering the samples for the second time to acquire at least one second category and the samples contained by each second category, wherein a second distance threshold value is larger than a first distance threshold value; dividing the samples in the second categories containing one sample only into isolated samples; selecting one sample in each first category as a representative sample, and setting the training samples of the SVM classifier according to the representative samples and the isolated samples. By the SVM classifier training sample acquiring method, the training method and the training system, number of the samples can be reduced effectively, complexity of sample space of the classifier is lowered, and classifier training is enabled to be simpler and more effectively.

Description

technical field [0001] The present invention relates to the technical field of SVM classifiers, in particular to a method for obtaining training samples of an SVM classifier and a system thereof, as well as a method for training an SVM classifier and a system thereof. Background technique [0002] SVM (Support Vector Machine) was first proposed by Cortes and Vapnik in 1995. It shows many unique advantages in solving small samples, nonlinear and high-dimensional pattern recognition, and can be extended to other machine learning such as function fitting. problem. [0003] SVM is based on the VC dimension (vapnik-chervonenkis dimension) theory of statistical learning theory and the principle of structural risk minimization, according to the complexity of the model (that is, the learning accuracy of specific training samples) and learning ability ( That is, the ability to identify any sample without error) to seek the best compromise in order to obtain the best generalization a...

AI Technical Summary

Problems solved by technology

[0009] In view of the complexity of the sample space of the classifier in the above-mentioned background technology and the problem that it is easily affected by noise samples, the purpose of the present invention is to provide a method and system for obtaining training samples for SVM classifiers, which can reduce the number of classifiers. The complexity of the sample space, and reduce the impact of noisy samples on classifier training

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