Text classification method based on sample scaling

Abstract

The invention discloses a text classification method based on sample scaling. The method comprises the following steps: calculating the distance from a data sample to a classification hyperplane, finding a sample far away from the classification surface of the support vector machine, deleting the sample, endowing corresponding weights to the remaining samples according to the distance, and training the support vector machine by using the weighted data samples. According to the classification method provided by the invention, the sample data is firstly reduced, and then the data is correspondingly weighted so as to be used for text classification in a support vector machine. The influence of noise data on support vector machine classification can be reduced, the noise immunity of the modelis improved, the number of support vectors is reduced, and better text classification accuracy is obtained.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a method for classifying text based on sample scaling. Background technique [0002] A support vector machine is a machine learning algorithm based on statistical learning theory. In recent years, it has attracted much attention due to its good generalization performance. In practical applications, support vector machines have shown higher performance than traditional learning machines. Therefore, support vector machines have been widely used in pattern recognition and functional regression. However, both the traditional SVM and its improved versions assume that the samples in a given training set have equal contributions to constructing the optimal separating hyperplane. However, in many practical engineering applications, the obtained training data is often polluted by noise. Therefore, dealing with noise in large-scale training data has become a...

AI Technical Summary

Problems solved by technology

This method effectively improves the accuracy, but is limited by the computational cost and simplified transformation form, which affects the speed of classification

[0004] Although the existing methods have made great improvements on the traditional support vector machine method and have shown their respective advantages in processing large sample data sets, they ignore that there are only training samples near the final separating hyperplane in the training data set. Points may become support vectors, and most of the remaining training sample points play a small or no role in training support vector machines.

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