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Method and device for training supervised machine learning model

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: 2018-03-30
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 device for training supervised machine learning model
  • Method and device for training supervised machine learning model
  • Method and device for training supervised machine learning model

Examples

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Embodiment Construction

[0013] figure 1 A flowchart illustrating an example method for training a model for supervised machine learning according to an embodiment of the present disclosure. Such as 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 including the motion state of the same target object at different time points within one or more time periods; Step S105, recording annotation data related to the movement of the target object in one or more time periods during the process of generating multiple artificial images; Step S110, generating a multimedia stream including motion based on the multiple artificial images; Step S115, Using the data of multiple frames of the multimedia stream as multiple input data of the model to perform calculations in the model to obtain derivation data related to motion; and step S120 , comparing the derivation data and the anno...

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Abstract

The invention discloses a method and a device for training a supervised machine learning model. The method includes the steps: generating a plurality of man-made images comprising movement states of same target objects at different time points in one or more time periods; recording annotation data related to movement of the target objects in one or more time periods in the generating process of the man-made images; generating multimedia streaming comprising movement based on the man-made images; using a plurality of frame data of the multimedia streaming as a plurality of input data of the model to execute operation in the model and acquire derivation data related to movement; comparing the derivation data with the annotation data to determine whether to adjust parameters of the model or not. By the method, a large quantity of manual annotation needed in the training process of the model can be omitted.

Description

technical field [0001] The present disclosure generally relates to the technical field of supervised machine learning models, and in particular to methods and apparatuses for training supervised machine learning models. Background technique [0002] Supervised machine learning (supervised machine learning) usually needs to use a large number of training samples to train the model, and determine whether to adjust the parameters of the model according to the comparison between the expected results and the derivation 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 adapted to other data (for example, actual application data) other than the training samples. Supervised machine learning models may include, for example, artificial neural networks (eg, convolutional neural networks), decision trees, and the like. [0003] Many different training sample sets or training sample banks have been provid...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N99/00G06N3/08
CPCG06N3/08G06N20/00
Inventor 颜沁睿
Owner SHENZHEN HORIZON ROBOTICS TECH CO LTD
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