Method and system for training model and method and system for predicting sequence data

A technology for sequence data and sequence prediction, applied in the field of using machine learning models to predict sequence data, can solve the problem that the HMM model cannot handle the diversity of sequence patterns of training data at the same time, so as to ensure the diversity of sequence patterns, improve prediction accuracy, overcome the scarcity effect

Active Publication Date: 2021-03-19
THE FOURTH PARADIGM BEIJING TECH CO LTD
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AI Technical Summary

Problems solved by technology

[0005] The present invention aims to solve the problem that the existing HMM model cannot simultaneously deal with the scarcity of training data and the diversity of sequence patterns of different objects, for example, to improve the prediction accuracy of sequence data in scenarios involving object sequence data (e.g., sequence behavior) prediction sex

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  • Method and system for training model and method and system for predicting sequence data
  • Method and system for training model and method and system for predicting sequence data
  • Method and system for training model and method and system for predicting sequence data

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Embodiment Construction

[0022] In order to enable those skilled in the art to better understand the present application, the exemplary embodiments of the present application will be described in further detail below in conjunction with the accompanying drawings and specific implementation methods.

[0023] figure 1 is a block diagram showing a system 100 for training a machine learning model for predicting sequence data (hereinafter, for convenience of description, it will be simply referred to as a "model training system") 100 according to an exemplary embodiment of the present application. like figure 1 As shown, the model training system 100 may include a training sample acquisition device 110 and a training device 120 .

[0024] Specifically, the training sample obtaining means 110 may obtain a sequence training sample set. Here, the sequence training sample set may include a plurality of pieces of sequence training samples for each of the plurality of objects, and each sequence training sample...

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Abstract

A method and system for training a model and a method and system for predicting sequence data are provided. The method and system for training a model can obtain a sequence training sample set, and train a machine learning model based on the sequence training sample set, wherein the machine learning model is a hidden Markov model including two hidden state layers, wherein the first hidden state layer The state layer includes a personalized hidden state for each of the plurality of objects, and the second hidden state layer includes a plurality of shared hidden states shared by the plurality of objects. The method and system for predicting sequence data can obtain sequence prediction samples of objects, and use the machine learning model to perform prediction on the sequence prediction samples to provide prediction results about the next sequence data after the plurality of sequence data, Wherein, the machine learning model is trained in advance to predict the next sequence of data after the series of sequence data in chronological order.

Description

technical field [0001] The present application generally relates to the field of artificial intelligence, and more specifically, relates to a method and system for training a machine learning model for predicting sequence data, and a method and system for predicting sequence data using a machine learning model. Background technique [0002] With the emergence of massive data, artificial intelligence technology has developed rapidly, and machine learning is an inevitable product of the development of artificial intelligence to a certain stage. It is committed to mining valuable potential information from large amounts of data through computing means. [0003] It is very important for various application scenarios to mine the regularity behind sequence data by modeling continuously occurring sequence data (e.g., mobile location data and music listening sequences, etc.) through machine learning. For example, personalized sequence behavior is ubiquitous in our daily life, and si...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor 姚权铭时鸿志
Owner THE FOURTH PARADIGM BEIJING TECH CO LTD
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