Model optimization method, data identification method and data identification device
AssigneeFujitsu Limited
InventorsMengjiao Wang, Rujie Liu
Life of this patent
Estimating the term…
Abstract
The present disclosure relates to a model optimization method, a data identification method and a data identification device. A method for optimizing a data identification model comprises: acquiring a loss function of a data identification model to be optimized; calculating weight vectors in the loss function which correspond to classes; performing normalization processing on the weight vectors; updating the loss function by increasing an included angle between any two of the weight vectors; optimizing the data identification model to be optimized based on the updated loss function.
Claims13
A method for optimizing a data identification model, comprising:
- acquiring a loss function of a data identification model to be optimized;
- calculating weight vectors in the loss function which correspond to classes;
- performing normalization processing on the weight vectors;
- updating the loss function by increasing an included angle between any two of the weight vectors;
- and
- optimizing the data identification model to be optimized based on the updated loss function.
A data identification method, comprising:
- performing data identification using an optimized data identification model obtained by a method for optimizing a data identification model comprising:
- acquiring a loss function of a data identification model to be optimized;
- calculating weight vectors in the loss function which correspond to classes;
- performing normalization processing on the weight vectors;
- updating the loss function by increasing an included angle between any two of the weight vectors;
- and
- optimizing the data identification model to be optimized based on the updated loss function.
A computer readable recording medium having stored thereon program instructions that, when executed by a computer, are used for implementing a method for optimizing a data identification model comprising:
- acquiring a loss function of a data identification model to be optimized;
- calculating weight vectors in the loss function which correspond to classes;
- performing normalization processing on the weight vectors;
- updating the loss function by increasing an included angle between any two of the weight vectors;
- and
- optimizing the data identification model to be optimized based on the updated loss function.
Dependencies are inferred from the claim text.