Increment study classification method under limited storage resources

Abstract

The invention provides an increment study classification method under limited storage resources, which belongs to the technical field of pattern recognition. The method adopts a minimum distance classifier and comprises the following concrete steps: firstly, pre-classifying new samples, adding the new samples which are correctly pre-classified to the corresponding subset of the classifier, adding the new samples which are wrongly pre-classified to the set of wrong samples, and carrying out K-mean clustering on the samples in the set of wrong samples; then respectively selecting representative samples for each subset in the classifier and the set of wrong samples, adding the subsets in the set of wrong samples to the classifier after selection, and updating the classifier; and finally, adopting the updated classifier to classify the new samples. In the invention, by selecting the representative samples, not only the studied knowledge is saved, but also the new knowledge is acquired, and higher sample recognizing accuracy is achieved on the basis of reducing the storage cost and calculating the cost.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and in particular relates to an incremental learning classification method. In the case of limited storage resources, the method continuously updates a classifier by learning new samples to improve the classification ability. Background technique [0002] With the rapid development of the network and the sharp increase in the amount of information, the traditional information mining and knowledge acquisition technology has been greatly challenged, and the data classification technology with incremental learning function is gradually becoming the key technology for the intelligent discovery and mining of current information First, compared with ordinary data classification technology, incremental learning classification technology has significant advantages, which are mainly reflected in two aspects: on the one hand, because it does not need to save historical data, it reduces the occupation of s...

AI Technical Summary

Problems solved by technology

[0003] Various incremental learning methods proposed in recent years have large or small defects, specifically: (1) when implementing incremental learning, previously trained samples are required to occupy storage space; (2) cannot Add new category information during learning, and the classification performance is poor; (3) Based on an online learning method, the iterative method of learning one sample after another is used to achieve incremental learning, and the classification efficiency is low

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