A classification model training method, device, and classification method and device

A classification model and training method technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as inability to accurately classify input sequences, inability to achieve accuracy, and poor RNN network memory capacity.

Active Publication Date: 2020-05-19
GUOXIN YOUE DATA CO LTD
View PDF9 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the memory ability of the RNN network is poor. When the input sequence is long, it cannot accurately classify the input sequence and cannot meet the use requirements for accuracy.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A classification model training method, device, and classification method and device
  • A classification model training method, device, and classification method and device
  • A classification model training method, device, and classification method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only It is a part of the embodiments of this application, not all of them. The components of the embodiments of the application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The application provides a classification model training method, device, and classification method and device. The classification model training method includes: obtaining a preset number of training image frames in a training video with labels; using a target neural network to perform feature learning on the training image frames, Extracting feature vectors for training image frames; based on the memory feature vectors corresponding to different classifications stored in the memory network, classify the training videos represented by the extracted feature vectors to obtain the classification results of the training videos; according to the classification results of the training videos and the training videos The comparison results between the labels of the target neural network are trained. The memory network has a strong memory ability, which can make up for the poor memory ability of the neural network itself. Regardless of the input sequence of any length, the memory network can memorize its characteristics well, so no matter what the input sequence is, it will not be affected. The recognition accuracy of the classification model will be affected due to the poor memory ability of the neural network.

Description

technical field [0001] The present application relates to the technical field of machine learning, in particular, to a classification model training method and device, and a classification method and device. Background technique [0002] Multi-layer feedback (Recurrent neural Network, RNN) neural network, also known as recurrent neural network, is an artificial neural network in which nodes are directional connected into a ring, which can use internal memory to process input sequences of any time sequence, and in video action prediction been widely applied. [0003] However, the memory ability of the RNN network is poor. When the input sequence is long, it cannot accurately classify the input sequence and cannot meet the use requirements for accuracy. Contents of the invention [0004] In view of this, the purpose of the embodiments of the present application is to provide a classification model training method and device, and a classification method and device, which can...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/24G06F18/214
Inventor 孙源良夏虎李长升刘萌
Owner GUOXIN YOUE DATA CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap