Method for building multi-classification support vector machine classifier based on Bhattacharyya distance and directed acyclic graph

Abstract

The invention discloses a method for building a multi-classification support vector machine classifier based on the Bhattacharyya distance and a directed acyclic graph, belonging to the field of mode identification. The method aims to solve the problems that the training speed is lowered when the number of training samples or the number of classes is increased and the calculation amount is large because the traditional multi-classification strategy can not utilize prior information with different separabilities among classes because of the fixed structure. The method disclosed by the invention comprises the following steps: 1. respectively calculating the Bhattacharyya distance between every two classes in a training sample for a multi-classification object; 2. building an initial operation form according to the Bhattacharyya distance between the every two classes in the training sample, which is obtained in the step 1; 3. according to the initial operation form obtained in the step 2, building a multiple classifier based on the directed acyclic graph structure; and 4. adopting a support vector machine as a binary classifier, and carrying out multi-classification based on the directed acyclic graph structure.

Description

technical field [0001] The invention relates to a method for constructing a multi-category support vector machine classifier based on Bhattacharydi distance and a directed acyclic graph, which belongs to the field of pattern recognition. Background technique [0002] As a powerful machine learning method, the Support Vector Machine (SVM) proposed by Viadimir N. Vapnik and his colleagues has been successfully applied in pattern recognition, especially in the field of fault diagnosis and classification. SVM adopts the Structural Risk Minimization Principle based on statistical learning theory, which can effectively solve problems such as nonlinearity, limited samples and high dimensions, and usually provides good learning ability and generalization ability. The support vector machine was originally proposed for binary classification problems and cannot be directly used for multi-class classification problems. Most of the fault diagnosis problems are multi-class classification ...

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Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that the traditional multi-classification strategy cannot use prior information with different separability between categories due to its fixed structure, and the training speed slows down with the increase of the number of training samples or categories, and the problem of large amount of calculation is provided. A method of constructing multi-category support vector machine classifier based on Bhattacharyachian distance and directed acyclic graph

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