Method and apparatus for training a model for supervised machine learning

A machine learning and supervised technology, applied in the field of supervised machine learning models, can solve the problems of high cost, low accuracy and low efficiency, and achieve the effect of improving efficiency, improving accuracy and reducing cost

Active Publication Date: 2022-07-08
SHENZHEN HORIZON ROBOTICS TECH CO LTD
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

The cost of training is high, but the accuracy and efficiency are low

Method used

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  • Method and apparatus for training a model for supervised machine learning
  • Method and apparatus for training a model for supervised machine learning
  • Method and apparatus for training a model for supervised machine learning

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Embodiment Construction

[0013] figure 1 A flowchart illustrating an example method for training a model for supervised machine learning in accordance with embodiments of the present disclosure. like figure 1 As shown, the exemplary method 100 according to an embodiment of the present disclosure may include: step S101, generating a plurality of artificial images, each artificial image containing the motion state of the same target object at different time points in one or more time periods; Step S105, record the annotation data related to the movement of the target object in one or more time periods in the process of generating a plurality of artificial images; Step S110, generate a multimedia stream including the movement based on the plurality of artificial images; Step S115, The operation in the model is performed using data of multiple frames of the multimedia stream as multiple input data of the model to obtain derived data related to motion; and step S120, the derived data and the annotation da...

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Abstract

A method and apparatus for training a supervised machine learning model is disclosed. The method includes: generating a plurality of artificial images, each artificial image containing the motion state of the same target object at different time points in one or more time periods; recording the movement state of the same target object at one or more time points in the process of generating the plurality of artificial images; or motion-related annotation data over multiple time periods; generate a multimedia stream including motion based on multiple artificial images; use data from multiple frames of the multimedia stream as multiple input data to the model to perform operations in the model to Obtaining derived data related to the motion; and comparing the derived data with the labeled data to determine whether to adjust parameters of the model. Through this method, a large number of manual annotations required in the training process of the model can be omitted.

Description

technical field [0001] The present disclosure relates generally to the technical field of supervised machine learning models, and in particular to methods and apparatus for training supervised machine learning models. Background technique [0002] Supervised machine learning usually involves training a model with a large number of training samples, and determining whether the parameters of the model need to be adjusted based on the comparison between the expected results and the inference results obtained by the model using the training samples And how to adjust the parameters of the model, so that the model can be well suited for other data outside the training samples (for example, the actual application data). Models for supervised machine learning may include, for example, artificial neural networks (eg, convolutional neural networks) and decision trees, among others. [0003] Many different sets or libraries of training samples have been provided. Designers of supervi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N20/00G06N3/08
CPCG06N3/08G06N20/00
Inventor 颜沁睿
Owner SHENZHEN HORIZON ROBOTICS TECH CO LTD
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