Cross merge method for reducing support vector and training time
A technology of support vector and training time, applied in the fields of instruments, digital data processing, computers, etc., can solve the problems of reducing learning time, long training time, reducing support vector, etc., and achieve the effect of reducing training samples and training time.
Inactive Publication Date: 2007-12-05
SHANGHAI JIAOTONG UNIV
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[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
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[0037] The present invention will be further described in the manner of example below in conjunction with accompanying drawing:
[0038] As shown in Figure 1, if it is a multi-class problem, multi-class and two-class conversion is 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=PYN, where P and N denote the training 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 . For example, in Figure 2, a chessboard of [0, 200]×[0, 200] is divided into four ...
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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...
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IPC IPC(8): G06F15/18
Inventor 文益民吕宝粮
Owner SHANGHAI JIAOTONG UNIV