公开(公告)号：US20230072240A1

公开(公告)日：2023-03-09

申请号：US17988168

申请日：2022-11-16

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Kafeng WANG ,  Chengzhong XU ,  Haoyi XIONG ,  Xingjian LI ,  Dejing DOU

IPC: G06K9/62

Abstract: A method for processing synthetic features is provided, and includes: the synthetic features to be evaluated and original features corresponding to the synthetic features are obtained. A feature extraction is performed on the synthetic features to be evaluated based on a number S of pre-trained samples, to obtain meta features with S samples. S is a positive integer. The meta features are input into the pre-trained meta feature evaluation model for a binary classification prediction, to obtain a probability of binary classification. Quality screening is performed on the synthetic features to be evaluated according to the probability of the binary classification, to obtain second synthetic features to be evaluated. The second synthetic features are classified in a good category. The second synthetic features and original features are input into a first classifier for evaluation. classified in a poor category.

公开(公告)号：US20220392199A1

公开(公告)日：2022-12-08

申请号：US17819777

申请日：2022-08-15

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD. ,  State Key Laboratory of Internet of Things for Smart City (University of Macau)

Inventor： Kafeng WANG ,  Chengzhong XU ,  Haoyi XIONG ,  Xingjian LI ,  Dejing DOU

IPC: G06V10/774 ,  G06V10/764 ,  G06V10/82 ,  G06V10/778

Abstract: A method and an apparatus for training a classification model and data classification includes: obtaining a sample set and a pre-trained classification model, wherein the classification model includes at least two convolutional layers, each convolutional layer is connected to a classification layer through a fully connected layer; inputting the sample set into the classification model, and obtaining a prediction result output by each classification layer, wherein the prediction result includes a prediction probability of a class to which each sample belongs; calculating a probability threshold of each classification layer based on the prediction result output by each classification layer; setting a prediction stopping condition for the classification mode according to the probability threshold of each classification layer.