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. 10, 2023

Filed:

Aug. 24, 2018
Applicant:

Alibaba Group Holding Limited, Grand Cayman, KY;

Inventors:

Xiaoyan Jiang, Hangzhou, CN;

Xu Yang, Hangzhou, CN;

Bin Dai, Hangzhou, CN;

Wei Chu, Hangzhou, CN;

Assignee:

Alibaba Group Holding Limited, Grand Cayman, KY;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06K 9/62 (2022.01); G06N 3/08 (2006.01); G06Q 30/06 (2012.01); G06Q 30/02 (2012.01); G06Q 30/00 (2012.01);
U.S. Cl.
CPC ...
G06K 9/6257 (2013.01); G06K 9/6262 (2013.01); G06K 9/6277 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 30/018 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/06 (2013.01);
Abstract

The present disclosure provides methods and an apparatuses for building a data identification model. One exemplary method for building a data identification model includes: performing logistic regression training using training samples to obtain a first model, the training samples comprising positive and negative samples; sampling the training samples proportionally to obtain a first training sample set; identifying the positive samples using the first model, and selecting a second training sample set from positive samples that have identification results after being identified using the first model; and performing Deep Neural Networks (DNN) training using the first training sample set and the second training sample set to obtain a final data identification model. The methods and the apparatuses of the present disclosure improve the stability of data identification models.


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