The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 06, 2022

Filed:

Dec. 20, 2019
Applicant:

Beijing Baidu Netcom Science and Technology Co., Ltd., Beijing, CN;

Inventors:

Guangyao Han, Beijing, CN;

Xingbo Chen, Beijing, CN;

Guobin Xie, Beijing, CN;

Yanjiang Liu, Beijing, CN;

Fu Qu, Beijing, CN;

Liqiang Xue, Beijing, CN;

Jin Zhang, Beijing, CN;

Wenjing Qin, Beijing, CN;

Xiaolan Luo, Beijing, CN;

Hongjiang Du, Beijing, CN;

Zeqing Jiang, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/20 (2019.01); G06F 9/38 (2018.01); G06K 9/62 (2022.01);
U.S. Cl.
CPC ...
G06N 20/20 (2019.01); G06F 9/3867 (2013.01); G06K 9/6262 (2013.01);
Abstract

Embodiments of the present disclosure relate to a method and apparatus for generating information. The method may include: receiving a modeling request; determining a target number of initial machine learning pipelines according to a type of training data and a model type; and executing following model generation steps using the target number of initial machine learning pipelines: generating a target number of new machine learning pipelines based on the target number of initial machine learning pipelines; performing model training based on the training data, the target number of initial machine learning pipelines, and the target number of new machine learning pipelines, to generate trained models; evaluating the obtained trained models respectively according to the evaluation indicator; determining whether a preset training termination condition is reached; and determining, in response to determining the preset training termination condition being reached, a target trained model from the obtained trained models according to evaluation results.


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