Sample transfer learning method and device

A technology of transfer learning and samples, which is applied in the field of sample transfer learning methods and devices, can solve the problems that the sample set of the target field cannot be reliably determined, and the performance of the prediction task of the target field cannot be effectively improved, so as to achieve the effect of improving performance

Active Publication Date: 2020-08-25
度小满科技(北京)有限公司
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  • Application Information

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Problems solved by technology

However, based on the current sample transfer learning method, the sample set of the target field cannot be reliably

Method used

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  • Sample transfer learning method and device
  • Sample transfer learning method and device
  • Sample transfer learning method and device

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Embodiment Construction

[0058] The scheme of this application is suitable for sample migration based on prediction tasks, such as sample migration of classification tasks or sample migration based on regression tasks. Through the scheme of this application, sample migration can be realized more reliably and effectively, so that samples based on the target field The set can more effectively improve the performance of the prediction task in the target domain.

[0059] 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. Apparently, the described embodiments are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0060] Such as figure 1 As sho...

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Abstract

The invention provides a sample transfer learning method and device, and the method combines the prediction results of a prediction model of a source field and a prediction model of a target field ona sample in a sample transfer process, and gives full consideration to the impact on the prediction precision of the prediction model of the target field from the sample of the source field. Meanwhile, in the sample migration iteration process, the prediction models of the source domain and the target domain are continuously optimized based on the intermediate state sample set of the source domainand the target domain obtained by migration; and the performance of the prediction model of each iteration is not inferior to the performance of the prediction model in the last iteration, so that the finally migrated sample set of the target field is more beneficial to improving the performance of the prediction task of the target field.

Description

technical field [0001] The present application relates to the technical field of data processing, and more specifically relates to a sample transfer learning method and device. Background technique [0002] Generally speaking, transfer learning is to use existing knowledge to learn new knowledge, and the core is to find the similarity between existing knowledge and new knowledge. In transfer learning, our existing knowledge is called the source domain, and the new knowledge to be learned is called the target domain. The source domain and the target domain are different but related. We need to find and use the correlation between the source domain and the target domain for knowledge transfer to achieve data calibration. [0003] Among them, the goal of sample transfer learning is to find samples that can improve the performance of target domain prediction tasks (such as classification prediction tasks or regression prediction tasks, etc.) from the sample set in the source do...

Claims

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Application Information

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IPC IPC(8): G06N20/00G06K9/62
CPCG06N20/00G06F18/214G06F18/24
Inventor 林熙东
Owner 度小满科技(北京)有限公司
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