Mobile ai
AssigneeInternational Business Machines Corporation
InventorsUmar Asif, Stefan von Cavallar, Jianbin Tang, Stefan Harrer
Life of this patent
Estimating the term…
Abstract
A machine learning model can be updated based on collected data (i.e., initially unlabeled data). The unlabeled data can be labeled based on comparisons to labeled data. The newly labeled data, referred to as “weak labeled data” (as it was labeled without direct input of a professional) can then be used as training data in order to retrain the machine learning model.
Claims20
A method of improving performance of a machine learning model, the method comprising:
- obtaining labeled data;
- obtaining unlabeled data;
- comparing the labeled data and the unlabeled data;
- labeling, based on the comparing, the unlabeled data, resulting in weak-labeled data;
- retraining, based on the weak-labeled data, a model, resulting in a retrained model.
A system, comprising:
- a memory;
- and
- a central processing unit (CPU) coupled to the memory, the CPU configured to execute instructions to:
- obtain labeled data;
- obtain unlabeled data;
- compare the labeled data and the unlabeled data;
- label, based on the comparing, the unlabeled data, resulting in weak-labeled data;
- retrain, based on the weak-labeled data, a model, resulting in a retrained model.
A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:
- obtain labeled data;
- obtain unlabeled data;
- compare the labeled data and the unlabeled data;
- label, based on the comparing, the unlabeled data, resulting in weak-labeled data;
- retrain, based on the weak-labeled data, a model, resulting in a retrained model.
Dependencies are inferred from the claim text.