Classification method based on semi-supervised extreme learning machine with deep structure

Abstract

The invention provides a classification method based on a semi-supervised extreme learning machine with a deep structure. Firstly automatic feature learning is performed on original data by utilizing a deep structure to obtain advanced expression of data features; and then similarity measurement between marked and unmarked sample features is calculated, and new features are classified by utilizing the semi-supervised extreme learning machine, so that the classification accuracy is improved. On one hand, the problem that a cascaded extreme learning machine performs learning only by utilizing marked samples and ignores useful information in unmarked samples is solved; and on the other hand, the problems of insufficient learning of sample features and the like due to the fact that the semi-supervised extreme learning machine is limited by a single-layer network structure are solved and finally the purpose of improving the network generalization performance and the classification accuracy is achieved.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a classification method of a semi-supervised extreme learning machine with a deep structure. Background technique [0002] Pattern classification is a key technology in the BCI system. Currently, the most commonly used classification algorithms include K-nearest neighbor method, artificial neural network, naive Bayesian, support vector machine (SVM) and other algorithms. Extreme learning machine (Extreme learning machine, ELM) is a machine learning algorithm that has developed rapidly in recent years. It is essentially a single hidden layer feedforward neural network with simple structure, fast learning speed, nonlinear processing ability and Due to its good global search performance and other advantages, a large number of scholars have devoted themselves to its application in the BCI system and achieved good classification results. Although the ELM method has achieved some impo...

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Problems solved by technology

[0007] On the one hand, the invention solves the problem that the cascaded extreme learning machine only uses marked samples to learn, ignoring the useful information in the unmarked samples; Insufficient feature learning and other problems, and finally achieve the purpose of improving the generalization performance of the network and improving the classification accuracy

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