Active learning-based MADALINE neural network sample selection method and system

Abstract

The invention discloses an active learning-based MADALINE neural network sample selection method and system, and belongs to the technical field of machine learning in intelligent science and technology. On the basis of sensitivity, sample points with high sensitivity are selected, severely-changing sample points around the sample points with high sensitivity are searched for, and the sample points are important to a training classifier in most cases. The method provided by the invention can effectively reduce the number of sample points that need to be marked, reduces cost of marking and improves performance of the classifier.

Description

technical field [0001] The present invention relates to a learning sample selection method and its system when designing a MADALINE neural network, in particular to an active learning-based learning sample selection method and a device thereof that can effectively improve neural network classification efficiency, and belongs to machine learning technology in intelligent science and technology field. Background technique [0002] The MADALINE neural network is a type of neural network whose input, output, and activation function are all discrete values. When designing a MADALINE neural network classifier, the labeling of training samples is generally done by experts, which often costs a lot of money and time. In the past, when selecting samples to be labeled, they were generally randomly selected from the obtained unlabeled samples. Training a classifier in this way often requires a large number of labeled samples, which requires a lot of manpower and material resources, an...

AI Technical Summary

Problems solved by technology

However, the complexity of this algorithm is extremely high, and it is not applicable to many complex problems.

Images