Cross merge method for reducing support vector and training time
A technology of support vectors and training time, applied in instruments, electrical digital data processing, computers, etc., can solve the problems of reducing learning time, long training time, reducing support vectors, etc., to achieve the effect of reducing training samples and reducing training time
Inactive Publication Date: 2005-03-02
SHANGHAI JIAO TONG UNIV
View PDF0 Cites 2 Cited by
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
[0004] The purpose of the present invention is to provide a cross-merging method that reduces support vectors and training time for the existing problem of too long training time when using support vector machine methods to solve large-scale problems, so that it can reduce learning time and reduce support vectors
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 moreImage
Smart Image Click on the blue labels to locate them in the text.
Smart ImageViewing Examples
Examples
Experimental program
Comparison scheme
Effect test
Embodiment Construction
[0037] The present invention will be further described in the manner of example below in conjunction with accompanying drawing:
[0038] Such as figure 1 As shown, if it is a multi-class problem, multi-class and two-class conversions are required. Then the inventive method comprises the following steps:
[0039] First, the training samples are classified and extracted through the preprocessing of the training samples, and the samples belonging to each class form a set. This preprocessing process can be performed when collecting training samples, which can reduce the time complexity of the preprocessing process. In the case of two classes, the training samples are preprocessed into T=P∪N, where P and N represent the training sample sets belonging to the two classes respectively.
[0040] Second, decompose P and N according to the preset decomposition ratio r, and decompose them into P 1 ,P 2 and N 1 , N 2 . such as in figure 2 A [0, 200] × [0, 200] chessboard is divid...
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
Login to View More Abstract
The cross-merging method for reducing back-up vector and training time in the field of intelligent information processing technology includes three steps. The training set decomposing step includes classifying the training specimen sets, extracting specimen, decomposing each specimen set into two son sets, and combining the son sets to obtain four training sets. The layered data screening step based on back-up vector includes processing the four training sets in back-up vector machine method to obtain four back-up vector sets, merges the four back-up vector sets in cross-merging regulations into two groups as two training sets, parallelly processing two classified problems represented by these two training sets in back-up vector machine method to obtain two back-up vector sets, and merging the two back-up vector sets to obtain final training set. Training back-up vector machine with the final training set can obtain final classifier.
Description
technical field [0001] The invention relates to a layered parallel machine learning method based on the nature of support vectors, in particular to a cross-merging method for reducing support vectors and training time. It is used in the field of intelligent information processing technology. Background technique [0002] With the development of science and technology, human beings have accumulated a large amount of data in various fields, and these data are still increasing at a higher speed. The analysis and understanding of these data is of great significance to the further development of human society, and may even lead to more important discoveries of nature. On the other hand, due to the statistical learning theory as a solid theoretical foundation, the support vector machine method has become a widely popular pattern classification method. There are two approaches to solving large-scale pattern classification problems using support vector machine methods. The increm...
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
Login to View More Patent Type & Authority Applications(China)
IPC IPC(8): G06F15/18
Inventor 文益民吕宝粮
Owner SHANGHAI JIAO TONG UNIV