Distributed classification system

A system comprising:

• a processor;
• and
• a computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the system to
• send a first computation request to a plurality of worker computing devices;
• receive a first response to the first computation request from each worker computing device of the plurality of worker computing devices, wherein each first response includes a first matrix computed as a second order derivative of a logarithm of a predefined likelihood function on a subset of training data distributed to each respective worker computing device, wherein the training data includes a plurality of observation vectors, wherein each observation vector of the plurality of observation vectors includes a value for each variable of a plurality of variables;
• define a global first matrix by concatenating the first matrix from each worker computing device of the plurality of worker computing devices;
• compute a kernel matrix using an entirety of the training data and a predefined kernel function;
• send a second computation request to the plurality of worker computing devices, wherein the second computation request indicates that each worker computing device compute a classification probability for each observation vector of the plurality of observation vectors distributed to a respective worker computing device using the defined global first matrix and the computed kernel matrix;
• and
• output the determined classification probability for each observation vector of the plurality of classified observation vectors.

A method of distributed execution of a trained classification model, the method comprising:

• sending, by a computing device, a first computation request to a plurality of worker computing devices;
• receiving, by the computing device, a first response to the first computation request from each worker computing device of the plurality of worker computing devices, wherein each first response includes a first matrix computed as a second order derivative of a logarithm of a predefined likelihood function on a subset of training data distributed to each respective worker computing device, wherein the training data includes a plurality of observation vectors, wherein each observation vector of the plurality of observation vectors includes a value for each variable of a plurality of variables;
• defining, by the computing device, a global first matrix by concatenating the first matrix from each worker computing device of the plurality of worker computing devices;
• computing, by the computing device, a kernel matrix using an entirety of the training data and a predefined kernel function;
• sending, by the computing device, a second computation request to the plurality of worker computing devices, wherein the second computation request indicates that each worker computing device compute a classification probability for each observation vector of the plurality of observation vectors distributed to a respective worker computing device using the defined global first matrix and the computed kernel matrix;
• and
• outputting, by the computing device the determined classification probability for each observation vector of the plurality of classified observation vectors.