Computer architecture for identifying centroids using machine learning in a correlithm object processing system

A machine learning model training method, comprising:

• selecting, by a model training engine implemented by a processor, a first substring correlithm object from a set of sub-string correlithm objects, wherein:
• each sub-string correlithm object is represented by an n-bit digital word;
• and
• each sub-string correlithm object is adjacent inn-dimensional space to a preceding sub-string correlithm object and a subsequent sub-string correlithm object to form a string correlithm object;
• selecting, by the model training engine, a second sub-string correlithm object from the set of sub-string correlithm objects;
• computing, by the model training engine, a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object;
• comparing, by the model training engine, the Hamming distance to a bit difference threshold value;
• determining, by the model training engine, that the Hamming distance is less than the bit difference threshold value;
• computing, by the model training engine, an average of the first sub-string correlithm object and the second sub-string correlithm object in the n-dimensional space in response to the determination;
• based on the computed average, training, by the model training engine, a machine learning model such that the machine learning model defines the average as a centroid for the first cluster, wherein the machine learning model is configured to map a set of feature vector inputs to a plurality of clusters.

A non-transitory computer readable medium including a computer program comprising executable instructions that when executed by a processor causes the processor to:

• select a first sub-string correlithm object from a set of sub-string correlithm objects, wherein:
• each sub-string correlithm object is represented by an n-bit digital word;
• and
• each sub-string correlithm object is adjacent inn-dimensional space to a preceding sub-string correlithm object and a subsequent sub-string correlithm object to form a string correlithm object;
• select a second sub-string correlithm object from the set of sub-string correlithm objects;
• compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object;
• compare the Hamming distance to a bit difference threshold value;
• determine that the Hamming distance is less than the bit difference threshold value;
• compute an average of the first sub-string correlithm object and the second sub-string correlithm object in the n-dimensional space in response to the determination;
• based on the computed average, train a machine learning model such that the machine learning model defines the average as a centroid for the first cluster, wherein the machine learning model is configured to map a set of feature vector inputs to a plurality of clusters.