Text classification method based on sample scaling

A text classification and sample technology, applied in the field of artificial intelligence, can solve problems such as the speed and small effect of affecting classification, and achieve the effect of avoiding the impact.

Pending Publication Date: 2019-10-25
JIANGSU UNIV
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 respecti

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Text classification method based on sample scaling
  • Text classification method based on sample scaling
  • Text classification method based on sample scaling

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0026] Such as figure 1 As shown, a method of text classification based on sample scaling, the specific steps are as follows:

[0027] S1, obtain text data as a sample set, for the space R, set a training sample set T={(x i ,y i )}, where x i ∈X=R n ,y i ∈ Y = {+1, -1, ..., s}, i = 1, 2, ..., N, x i is the i-th eigenvector, also known as an instance, y i is x i The category label of (x i ,y i ) is called a sample point, and the training sample set has a total of N sample data objects, and these sample objects belong to S categories.

[0028] S2, use the training sample set T to train the...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F16/35G06K9/62
CPCG06F16/35G06F18/2411G06F18/214
Inventor 潘雨青翟文鹏李搏薛惠丹
Owner JIANGSU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap