Machine learning recognition method based on deep learning

A technology of machine learning and recognition methods, applied in the field of machine learning recognition based on deep learning, which can solve problems such as inability to classify and recognize, limited versatility, and limited application

Active Publication Date: 2018-06-29
重庆茂侨科技有限公司
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0006] In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is how to provide a machine learning recognition method based on deep learning to solve the problem that the existing multimedia data classification machine learning recognition method needs to rel

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  • Machine learning recognition method based on deep learning
  • Machine learning recognition method based on deep learning
  • Machine learning recognition method based on deep learning

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Embodiment Construction

[0048] In view of the fact that the existing multimedia data classification machine learning recognition method needs to rely on a large number of training samples, resulting in limited practical application, it is necessary to analyze the recognition principle of the existing machine learning recognition method to find out the cause of the problem. The existing classification machine learning recognition methods usually compare the samples to be identified with the comparison samples of known categories separately, calculate the similarity between the samples to be identified and the comparison samples, or calculate the difference between the samples to be identified and the comparison samples. The difference distance value is used to judge whether the sample to be identified and the comparison sample belong to the same category, so as to realize the category recognition of the sample to be identified. Such a machine learning recognition method, applied in the application scen...

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Abstract

The invention provides a machine learning recognition method based on deep learning. Multiple times of different learning training can be performed on a machine learning model f1 with a certain quantity of known classes of multimedia data samples in different contrast sample input arranging orders, the machine learning model f1 obtained by learning is used for recognizing multimedia data classes and selects a convolutional neural network model or a fully connected neural network model, dependence on a huge number of training samples is reduced substantially, the multimedia data classes not subjected to learning training can be expanded conveniently for class recognition, the problem that practical applicability and generality are limited due to dependence of the conventional machine learning method for multimedia data classification on the huge number of training samples and failure in direct classification recognition on the classes not subjected to learning training is solved well, and the machine learning recognition method can be applied to more specific multimedia data classification occasions more extensively and more effectively.

Description

technical field [0001] The invention relates to the fields of multimedia data processing technology and machine learning technology, in particular to a machine learning recognition method based on deep learning. Background technique [0002] Multimedia (Multimedia) is a combination of multiple media. In computer systems, multimedia refers to a human-computer interactive information exchange and dissemination media that combines two or more media. The media used include text, pictures, photos, and sound. , animations and videos, and the interactive features provided by the program. [0003] With the advent of the era of big data, the classification and mining technology of massive multimedia data is particularly important. In massive data mining, how to use the information classified and mined from existing data to guide the classification and mining of new data has become a new research hotspot. Especially when the number of samples for certain tasks is small, the use of m...

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Application Information

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IPC IPC(8): G06K9/62G06N99/00
CPCG06N20/00G06F18/241G06F18/214
Inventor 张杨徐传运许洲
Owner 重庆茂侨科技有限公司
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