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:
Jan. 02, 2024

Filed:

Oct. 04, 2022
Applicant:

Tencent Technology (Shenzhen) Company Limited, Shenzhen, CN;

Inventors:

Wei Zhao, Shenzhen, CN;

Yabing Feng, Shenzhen, CN;

Yu Liao, Shenzhen, CN;

Junbin Lai, Shenzhen, CN;

Haixia Chai, Shenzhen, CN;

Xuanliang Pan, Shenzhen, CN;

Lichun Liu, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 18/25 (2023.01); G06N 20/20 (2019.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01);
U.S. Cl.
CPC ...
G06N 20/20 (2019.01); G06F 18/217 (2023.01); G06F 18/2148 (2023.01); G06F 18/254 (2023.01); G06N 20/00 (2019.01);
Abstract

A machine learning model training method includes: training a machine learning model using features of samples in a training set, where a sample in the training set corresponds to an initial first weight and an initial second weight. In one iteration, the method includes: determining a first sample set comprising one or more samples whose corresponding target variables are incorrectly predicted; determining an overall predicted loss of the first sample set based on the predicted losses and corresponding first weights of samples in the first sample set; updating the first weights and second weights of the samples in the first sample set based on the overall predicted loss of the first sample set; and inputting the second weights, the features, and the target variables of the samples in the training set to the machine learning model, and initiating a next iteration of training the machine learning model.


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