A perceptron network data classification method based on an AdaBoost algorithm

Abstract

The invention relates to a perceptron network data classification method based on an AdaBoost algorithm. The perceptron network data classification method is characterized by comprising the followingsteps: (1) determining a training sample set; (2) calculating the output weight of the sample set in the weak classifier; (3) determining a classifier weight vector; (4) determining input of a sensorand an activation function; (5) calculating a weighted error rate; (6) updating the sample weight; (7) updating a classifier weight vector; And (8) taking a test set sample as an input, and sending the input to a sensor for classification to obtain a classification result. According to the method, the network connection weight of the sensor is updated and iterated by using the idea of integrated learning in the AdaBoost algorithm, and a plurality of weak classifiers are integrated into a strong classifier, so that the classification accuracy of the model is improved. According to multiple groups of data experiment results, the classification method for improving the timeliness of the model on the basis of ensuring the classification accuracy is provided for numerical classification.

Description

technical field [0001] The invention relates to the fields of machine learning and data mining, and mainly relates to a method for classifying data. Background technique [0002] At present, for data classification problems, most technologies can achieve high accuracy, but the classification process is unstable and the robustness is poor, and some models are too complex, resulting in too long training time. Although some models have simple algorithms and fast training time, they cannot guarantee the classification accuracy. In classic machine learning classification tasks, data features are generally extracted in advance. After extracting many features, correlation analysis must be performed on these features to find the features that best represent characters and remove features that are irrelevant to classification. However, with the rapid development of the computer Internet and the exponential growth of massive data, how to efficiently extract useful data features is ve...

AI Technical Summary

Problems solved by technology

Therefore, the technology put into use must ensure a high accuracy rate and a simple algorithm process, because if the model is too complex, it will cost a lot of money and time

If the identification is wrong, even a small mistake, it may cause a series of irreparable losses

Images