CLASSIFIER TRAINING METHOD, SYSTEM AND DEVICE, AND DATA PROCESSING METHOD, SYSTEM AND DEVICE

摘要

Disclosed is a classifier training method. By using the method, the influence of noise labels can be reduced, and a classifier with a good classification effect can be obtained. The method comprises: acquiring a sample data set (401); dividing the sample data set into K sub-sample data sets, determining a group of data from the K sub-sample data sets to be a test data set, and taking the other sub-sample data sets, apart from the test data set, of the K sub-sample data sets as training data sets (402); training a classifier by means of the training data sets, and performing classification on the test data set by using the trained classifier, so as to obtain a second label of each sample in the test data set (403); acquiring a first index and a first hyper-parameter at least according to a first label and the second label (404); acquiring a loss function of the classifier at least according to the first hyper-parameter, wherein the loss function is used for updating the classifier (405); and when the first index satisfies a first preset condition, completing training of the classifier (406).