A parameter iteration method of artificial intelligence training includes: a setting a step, providing a training set, and setting a numerical range of the training parameters; an initializing step, selecting a three initial setting values from the numerical range of the training parameters, and calculating an accuracy values of each initial setting value according to the training set, setting the initial setting value with highest accuracy values as a first core value, and setting a first parameter range based on the first core value; the parameter optimization step, selecting three first iteration values in the first parameter range, calculating the accuracy value of each first training set value, comparing the accuracy value of each of the first training set values, setting the value with the highest accuracy value as the second core value, and setting the second parameter range based on the second core value; and the determining step, if the accuracy value of the second core value is higher than 0.9, setting the second core value as the training parameter standard value; if not higher than 0.9, repeats the parameter optimization step until the accuracy of a test core value is higher than 0.9, and then setting the test core value as the training parameter standard value.

Patent

S11201T0325

Biomdcare Corporation(柏瑞醫股份有限公司)

Enterprise

Experiment stage

2023/01/12

Negotiate by self,

2025/01/12