Methods and apparatus to process training data for an ai-based model
AssigneeMcAfee, Inc.
InventorNiall Fitzgerald
Life of this patent
Estimating the term…
Abstract
An example apparatus includes interface circuitry to obtain data; samples to train an AI-based model; machine readable instructions; and at least one programmable circuit to at least one of instantiate or execute the machine readable instructions to: transform the data samples into features; generate hash signatures for corresponding ones of the features; group the features into clusters based on the hash signatures; generate a filtered data set by filtering out features within a cluster of features having more than a threshold number of features; and train the AI-based model based on the filtered data set.
Claims20
An apparatus comprising:
- interface circuitry to obtain data samples to train an AI-based model;
- machine readable instructions;
- and
- at least one programmable circuit to at least one of instantiate or execute the machine readable instructions to:
- transform the data samples into features;
- generate hash signatures for corresponding ones of the features;
- group the features into clusters based on the hash signatures;
- generate a filtered data set by filtering out features within a cluster of features having more than a threshold number of features;
- and
- train the AI-based model based on the filtered data set.
A non-transitory computer readable medium comprising instructions to cause at least one programmable circuit to:
- generate hash signatures for corresponding data samples;
- group the data samples into clusters based on the hash signatures;
- generate a filtered data set by filtering out data samples within a cluster of data samples having more than a threshold number of data samples;
- and
- train an AI-based model based on the filtered data set.
A method comprising:
- transforming, by executing an instruction with programmable circuitry, data samples into features;
- generating, by executing an instruction with the programmable circuitry, hash signatures for corresponding ones of the features;
- grouping, by executing an instruction with the programmable circuitry, the features into clusters based on the hash signatures;
- generating, by executing an instruction with the programmable circuitry, a filtered data set by filtering out features within a cluster of features having more than a threshold number of features;
- and
- training, by executing an instruction with the programmable circuitry, an AI-based model based on the filtered data set.
Dependencies are inferred from the claim text.