Method and apparatus for model training

A technology for model training and clustering labels, applied to biological neural network models, character and pattern recognition, instruments, etc., can solve problems such as wrong samples, large model predictions, and low prediction accuracy

Inactive Publication Date: 2019-01-04
BEIJING DAJIA INTERNET INFORMATION TECH CO LTD
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Problems solved by technology

[0005] The technical problem to be solved by the embodiments of the present invention is to provide a method for model train

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  • Method and apparatus for model training
  • Method and apparatus for model training

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Embodiment Construction

[0050] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0051] refer to figure 1 , which shows a flow chart of steps of an embodiment of a method for model training in an embodiment of the present invention, which may specifically include the following steps:

[0052] Step 101, obtaining sample data to be trained in a specified classification category;

[0053] In an implementation manner, the designated classification category may be a classification category determined by a preset classification model, and / or, the designated classification category may also be a classification category manually designated, which is not limited in this embodiment of the present invention.

[0054] In an implementation manner, the specified classification category may be a coarse-grained classificatio...

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Abstract

The embodiment of the invention provides a method and a device for model training, wherein the method comprises the following steps: obtaining sample data to be trained in a designated classificationcategory; carrying out feature extraction on the sample data to be trained to obtain feature information corresponding to the designated classification category; clustering feature information corresponding to the designated classification category to obtain a plurality of clustering labels; performing data equalization processing on sample data corresponding to the clustering label; taking the sample data after the data equalization processing as the target sample data; Using the target sample data, the designated model is trained. The invention can refine the labels in the existing classification categories by the above-mentioned unsupervised method, Realize the sample balance within the category, provide balanced sample data for the model, according to the balanced sample data model training can be optimized model, using the optimized model for data forecasting can get more accurate forecasting results, improve the accuracy of model forecasting.

Description

technical field [0001] The present invention relates to the technical field of modeling, in particular to a model training method, a model training device, a model training system and one or more machine-readable media. Background technique [0002] Image classification is to distinguish different types of images according to the semantic information of images. It is an important basic problem in computer vision and the basis of other high-level visual tasks such as image detection, image segmentation, object tracking, and behavior analysis. [0003] In recent years, deep learning has been widely used in video images, speech recognition, natural language processing and other related fields. As an important branch of deep learning, convolutional neural network (CNN) has greatly improved the prediction accuracy of image classification tasks due to its strong fitting ability and end-to-end global optimization ability. [0004] Although the current image classification model ha...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/23G06F18/24G06F18/214
Inventor 张志伟王树强王希爱
Owner BEIJING DAJIA INTERNET INFORMATION TECH CO LTD
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