Scaling machine learning using approximate counting

A method performed by one or more computer devices, comprising:

• processing, by one or more processors of the one or more computer devices, each particular feature of a plurality of features in a repository, where processing each particular feature includes:
• performing, by one or more processors of the one or more computer devices, a plurality of different hash functions on a string, associated with the particular feature, to generate a corresponding plurality of different hash values,
• identifying, by one or more processors of the one or more computer devices, buckets, of a plurality of buckets in a memory, based on the plurality of different hash values,
• reading, by one or more processors of the one or more computer devices, a statistical value from each of the identified buckets,
• updating, by one or more processors of the one or more computer devices, each of the statistical values by subjecting each of the statistical values to a particular function to generate updated statistical values, and
• writing, by one or more processors of the one or more computer devices, each of the updated statistical values into a corresponding one of the identified buckets;
• identifying a group of features, of the plurality of features, based on the statistical values, including the updated statistical values, in the plurality of buckets;
• and
• using, by one or more processors of the one or more computer devices, the identified group of features and the statistical values associated with the identified group of features to make a prediction regarding particular data.

A system, comprising:

• one or more first memory devices to store information regarding a plurality of features;
• one or more second memory devices to store, in a plurality of memory locations, statistical values relating to the plurality of features;
• and
• one or more computer devices to:
• identify a particular feature of the plurality of features in the one or more first memory devices,
• subject a string, associated with the particular feature, to multiple, different hash functions to generate multiple, different hash values,
• identify, for each of the multiple, different hash values, a respective memory location, of the plurality of memory locations in the one or more second memory devices,
• read the statistical values stored in the respective memory locations,
• perform an operation on the read statistical values to obtain updated statistical values,
• write the updated statistical values into the respective memory locations, and
• use the statistical values, including the updated statistical values, to predict whether a particular e-mail includes spam or to predict whether a particular advertisement will be selected by a user.