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Neural network model training method and device, and transaction behavior risk identification method and device

A technology of neural network model and training method, which is applied in the field of neural network model training, transaction behavior risk identification method and device, can solve the problem of low training efficiency of neural network model, and achieve the effect of improving efficiency

Active Publication Date: 2018-10-09
ADVANCED NEW TECH CO LTD
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  • Application Information

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Problems solved by technology

However, the sample data collected above usually includes information of multiple dimensions, which leads to low efficiency of neural network model training

Method used

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  • Neural network model training method and device, and transaction behavior risk identification method and device
  • Neural network model training method and device, and transaction behavior risk identification method and device
  • Neural network model training method and device, and transaction behavior risk identification method and device

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Embodiment Construction

[0028] Embodiments of the present application are described below in conjunction with the accompanying drawings.

[0029] The neural network model training method provided in the embodiment of the present application is applicable to the scene of training a neural network model such as a deep neural network (DeepNeural Network, DNN) or an artificial neural network (Artificial Neural Network, ANN). The trained neural network model can be used in pattern recognition and classification scenarios, for example, it can be used to identify risks in trading behavior.

[0030] figure 1 It is a flowchart of a neural network model training method provided by an embodiment of the present application. The subject of execution of the method may be a device with processing capability: a server or a system or a device, such as figure 1 As shown, the method specifically includes:

[0031] Step 110 , input the multiple pre-collected sample data into a gradient boosting decision tree (Gradien...

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Abstract

The application relates to the technical field of the computer, and especially relates to a neural network model training method and device, and transaction behavior risk identification method and device. In the method for the neural network model training, multiple sample data collected in advance are inputted into the gradient boosting decision tree (GBDT), and the path information correspondingto each sample data in GBDT is determined. The training is performed on the neural network model according to the path information and the sample label corresponding to each sample data in GBDT. Thepath information is determined according to GBDT at first, then the neural network model is trained according to the path information and the sample label, one piece of the path information usually includes multiple dimension information of the sample data according to the characteristics of GBDT, and the efficiency of neural network model training can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a method and device for neural network model training and transaction risk identification. Background technique [0002] In the traditional technology, after the sample data is collected, the neural network model is trained directly according to the sample data and the sample labels of the sample data. However, the sample data collected above usually includes information of multiple dimensions, which leads to low efficiency of neural network model training. Contents of the invention [0003] This application describes a method and device for neural network model training and transaction behavior risk identification, which can improve the efficiency of neural network model training. [0004] In the first aspect, a neural network model training method is provided, including: [0005] Input a plurality of pre-collected sample data into the gradient boosting decis...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06K9/62
CPCG06N3/08G06F18/21G06F18/24
Inventor 李龙飞周俊李小龙
Owner ADVANCED NEW TECH CO LTD
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